{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Based on the published [notebook](https://github.com/mephistopheies/mlworkshop39_042017/blob/master/3_masterclass/ipy/feature_extraction.ipynb) by Pavel Nesterov; converted to Python 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "\n",
       "div.cell { /* Tunes the space between cells */\n",
       "margin-top:1em;\n",
       "margin-bottom:1em;\n",
       "}\n",
       "\n",
       "div.text_cell_render h1 { /* Main titles bigger, centered */\n",
       "font-size: 2.2em;\n",
       "line-height:1.4em;\n",
       "text-align:center;\n",
       "}\n",
       "\n",
       "div.text_cell_render h2 { /*  Parts names nearer from text */\n",
       "margin-bottom: -0.4em;\n",
       "}\n",
       "\n",
       "\n",
       "div.text_cell_render { /* Customize text cells */\n",
       "font-family: 'Times New Roman';\n",
       "font-size:1.5em;\n",
       "line-height:1.4em;\n",
       "padding-left:3em;\n",
       "padding-right:3em;\n",
       "}\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.core.display import HTML\n",
    "HTML(\"\"\"\n",
    "<style>\n",
    "\n",
    "div.cell { /* Tunes the space between cells */\n",
    "margin-top:1em;\n",
    "margin-bottom:1em;\n",
    "}\n",
    "\n",
    "div.text_cell_render h1 { /* Main titles bigger, centered */\n",
    "font-size: 2.2em;\n",
    "line-height:1.4em;\n",
    "text-align:center;\n",
    "}\n",
    "\n",
    "div.text_cell_render h2 { /*  Parts names nearer from text */\n",
    "margin-bottom: -0.4em;\n",
    "}\n",
    "\n",
    "\n",
    "div.text_cell_render { /* Customize text cells */\n",
    "font-family: 'Times New Roman';\n",
    "font-size:1.5em;\n",
    "line-height:1.4em;\n",
    "padding-left:3em;\n",
    "padding-right:3em;\n",
    "}\n",
    "</style>\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Процесс разработки решения топ-15%\n",
    "\n",
    "<center>Pavel Nesterov</center>\n",
    "<center>http://pavelnesterov.info/</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/Cellar/python3/3.6.3/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/lightgbm/__init__.py:46: UserWarning: Starting from version 2.2.1, the library file in distribution wheels for macOS is built by the Apple Clang (Xcode_8.3.1) compiler.\n",
      "This means that in case of installing LightGBM from PyPI via the ``pip install lightgbm`` command, you don't need to install the gcc compiler anymore.\n",
      "Instead of that, you need to install the OpenMP library, which is required for running LightGBM on the system with the Apple Clang compiler.\n",
      "You can install the OpenMP library by the following command: ``brew install libomp``.\n",
      "  \"You can install the OpenMP library by the following command: ``brew install libomp``.\", UserWarning)\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_columns', 500)\n",
    "import seaborn as sns\n",
    "sns.set_style(\"dark\")\n",
    "plt.rcParams['figure.figsize'] = 16, 12\n",
    "from tqdm import tqdm, tqdm_notebook\n",
    "import itertools as it\n",
    "import pickle\n",
    "import glob\n",
    "import os\n",
    "import string\n",
    "\n",
    "from scipy import sparse\n",
    "\n",
    "import nltk\n",
    "import spacy\n",
    "\n",
    "from sklearn.model_selection import StratifiedKFold, cross_val_score, GridSearchCV\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import log_loss, make_scorer\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "\n",
    "from scipy.optimize import minimize\n",
    "\n",
    "import eli5\n",
    "from IPython.display import display\n",
    "\n",
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/Cellar/python3/3.6.3/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/ipykernel_launcher.py:17: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id               int64\n",
      "is_duplicate     int64\n",
      "qid1             int64\n",
      "qid2             int64\n",
      "question1       object\n",
      "question2       object\n",
      "test_id          int64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "df_train = pd.read_csv('./../data/train.csv', \n",
    "                       dtype={\n",
    "                           'question1': np.str,\n",
    "                           'question2': np.str\n",
    "                       })\n",
    "df_train['test_id'] = -1\n",
    "df_test = pd.read_csv('./../data/test.csv', \n",
    "                      dtype={\n",
    "                          'question1': np.str,\n",
    "                          'question2': np.str\n",
    "                      })\n",
    "df_test['id'] = -1\n",
    "df_test['qid1'] = -1\n",
    "df_test['qid2'] = -1\n",
    "df_test['is_duplicate'] = -1\n",
    "\n",
    "df = pd.concat([df_train, df_test])\n",
    "df['question1'] = df['question1'].fillna('')\n",
    "df['question2'] = df['question2'].fillna('')\n",
    "df['uid'] = np.arange(df.shape[0])\n",
    "df = df.set_index(['uid'])\n",
    "print(df.dtypes)\n",
    "del(df_train, df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ix_train = np.where(df['id'] >= 0)[0]\n",
    "ix_test = np.where(df['id'] == -1)[0]\n",
    "ix_is_dup = np.where(df['is_duplicate'] == 1)[0]\n",
    "ix_not_dup = np.where(df['is_duplicate'] == 0)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.630802\n",
      "1    0.369198\n",
      "Name: is_duplicate, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(df[df['is_duplicate'] >= 0]['is_duplicate'].value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Исследуем задачу\n",
    "\n",
    "<img src=\"./../images/buben1.jpg\" />\n",
    "\n",
    "## Проверяем тестовый набор данных\n",
    "<br />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if len(glob.glob('./../data/tmp/0_*.csv')) < 5:\n",
    "    df_submit = df.loc[ix_test][['test_id', 'is_duplicate']].copy()\n",
    "    df_submit['is_duplicate'] = 0.0\n",
    "    df_submit.to_csv('./../data/tmp/0_zeros.csv', index=False)\n",
    "    df_submit['is_duplicate'] = 1.0\n",
    "    df_submit.to_csv('./../data/tmp/0_ones.csv', index=False)\n",
    "    df_submit['is_duplicate'] = 0.5\n",
    "    df_submit.to_csv('./../data/tmp/0_half.csv', index=False)\n",
    "    df_submit['is_duplicate'] = 0.25\n",
    "    df_submit.to_csv('./../data/tmp/0_quarter.csv', index=False)\n",
    "    df_submit['is_duplicate'] = 0.75\n",
    "    df_submit.to_csv('./../data/tmp/0_threequarters.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "score0 = 6.01888\n",
    "score1 = 28.52056\n",
    "score05 = 0.69315\n",
    "score025 = 0.47913\n",
    "score075 = 1.19485"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вспомним формуду риска гипотезы:\n",
    "$$\\large Q\\left(h\\right) = \\text{E}_{x, y \\sim p\\left(x, y\\right)}\\left[L\\left(h\\left(x\\right), y\\right)\\right]$$\n",
    "\n",
    "Тогда эмпирический риск имеет форму:\n",
    "$$\\large Q_{\\text{emp}}\\left(h\\right) = \\frac{1}{n}\\sum_{i = 1}^n L\\left(h\\left(x_i\\right), y_i\\right)$$\n",
    "\n",
    "Хотя если мы знаем, что распределение меток не равномерное, то можно это учесть, добавив вес примера:\n",
    "$$\\large Q_{\\text{emp}}\\left(h\\right) = \\sum_{i = 1}^n w_i L\\left(h\\left(x_i\\right), y_i\\right)$$\n",
    "- где $\\sum_{i=1}^n w_i = 1$\n",
    "\n",
    "В задаче Quora Question Pairs в качестве $L$ используется метрика logloss (вспоминаем логистическую регрессию), а гипотезы выдают вероятность принадлежности к классу дубликатов $h\\left(x_i\\right) = p_i$:\n",
    "$$\\large\\begin{array}{rcl}\n",
    "Q_{\\text{emp}} &=& \\frac{1}{n}\\sum_{i = 1}^n L\\left(h\\left(x_i\\right), y_i\\right) \\\\\n",
    "&=& -\\frac{1}{n}\\sum_{i = 1}^n y_i \\log p_i + \\left(1 - y_i\\right)\\log\\left(1 - p_i\\right) \\\\\n",
    "&=& -\\frac{1}{n}\\sum_{i \\in I_0} \\log\\left(1 - p_i\\right) - \\frac{1}{n}\\sum_{i \\in I_1} \\log p_i\n",
    "\\end{array}$$\n",
    "\n",
    "Предположим, что $\\forall p_i = 0$, уравнение <font color=\"blue\">(1)</font>:\n",
    "$$\\large\\begin{array}{rcl}\n",
    "q_0 &\\approx& -\\frac{1}{n}\\sum_{i \\in I_0} \\log\\left(1 - \\epsilon\\right) - \\frac{1}{n}\\sum_{i \\in I_1} \\log \\epsilon \\\\\n",
    "&=& -\\frac{n_0}{n}\\log\\left(1 - \\epsilon\\right) - \\frac{n_1}{n} \\log \\epsilon \\\\\n",
    "&=& -r_0\\log\\left(1 - \\epsilon\\right) - r_1 \\log \\epsilon\n",
    "\\end{array}$$\n",
    "\n",
    "$\\forall p_i = 1$, уравнение <font color=\"blue\">(2)</font>:\n",
    "$$\\large\\begin{array}{rcl}\n",
    "q_1 &\\approx& -r_0\\log \\epsilon - r_1 \\log \\left(1 - \\epsilon\\right)\n",
    "\\end{array}$$\n",
    "\n",
    "$\\forall p_i = \\frac{1}{2}$, уравнение <font color=\"blue\">(3)</font>:\n",
    "$$\\large\\begin{array}{rcl}\n",
    "q_{1/2} &=& -r_0\\log \\frac{1}{2} - r_1 \\log \\frac{1}{2} \\\\\n",
    "&=& \\left(r_0 + r_1\\right) \\log 2\n",
    "\\end{array}$$\n",
    "\n",
    "<font color=\"blue\">(1)</font> + <font color=\"blue\">(2)</font>:\n",
    "$$\\large\\begin{array}{rcl}\n",
    "-\\left(q_0+ q_1\\right) &=& r_0 \\log \\left(1 - \\epsilon\\right) + r_1 \\log \\epsilon + r_0 \\log \\epsilon + r_1 \\log \\left(1 - \\epsilon\\right) \\\\\n",
    "&=& \\left(r_0 + r_1\\right) \\log \\epsilon \\left(1 - \\epsilon\\right)\n",
    "\\end{array}$$\n",
    "\n",
    "Используя уравнение <font color=\"blue\">(3)</font>, вычисляем $\\epsilon$:\n",
    "$$\\large\\begin{array}{rcl}\n",
    "-\\left(q_0+ q_1\\right) &=& \\frac{q_{1/2}}{\\log 2} \\log \\epsilon \\left(1 - \\epsilon\\right) \\Leftrightarrow \\\\\n",
    "\\log \\epsilon \\left(1 - \\epsilon\\right) &=& \\frac{q_0 + q_1}{q_{1/2}} \\log \\frac{1}{2} \\Leftrightarrow \\\\\n",
    "\\log \\epsilon \\left(1 - \\epsilon\\right) &=& A \\Leftrightarrow \\\\\n",
    "\\epsilon^2 - \\epsilon + e^A &=& 0 \\Rightarrow \\\\\n",
    "\\epsilon &=& \\frac{1}{2}\\left(1 \\pm \\sqrt{1 - 4e^A}\\right)\n",
    "\\end{array}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = (score0 + score1)*np.log(0.5)/score05\n",
    "print('A =', A)\n",
    "print('eps_0 =', (1 + np.sqrt(1 - 4*np.exp(A)))/2)\n",
    "print('eps_1 =', (1 - np.sqrt(1 - 4*np.exp(A)))/2)\n",
    "eps = 10e-16\n",
    "print('eps =', eps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Для простоты написаня введем еще две константы:\n",
    "$$\\large\\begin{array}{rcl}\n",
    "B &=& \\log \\left(1 - \\epsilon\\right) \\\\\n",
    "C &=& \\log \\epsilon\n",
    "\\end{array}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "B = np.log(1 - eps)\n",
    "print('B =', B)\n",
    "C = np.log(eps)\n",
    "print('C =', C)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Решая систему из двух уравнений <font color=\"blue\">(1)</font> и <font color=\"blue\">(2)</font>, получим:\n",
    "$$\\large\\begin{array}{rcl}\n",
    "r_1 &=& \\frac{B\\left(q_1 - \\frac{C}{B}q_0\\right)}{C^2 - B^2} \\\\\n",
    "r_0 &=& \\frac{1}{C}\\left(-q_1 - r_1 B\\right)\n",
    "\\end{array}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "r1 = (score1 - (C/B)*score0) / ((C*C/B) - B)\n",
    "print('r1 =', r1)\n",
    "r0 = (-score1 - r1*B)/C\n",
    "print('r0 =', r0)\n",
    "print('r0 + r1 =', r0 + r1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "if not os.path.isfile('./../data/tmp/0_174.csv'):\n",
    "    df_submit = df.loc[ix_test][['test_id', 'is_duplicate']].copy()\n",
    "    df_submit['is_duplicate'] = 0.17426442474\n",
    "    df_submit.to_csv('./../data/tmp/0_174.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- score0174 = **0.46258**\n",
    "- на момент сабмита это **1208/1880**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Корректируем прогноз модели\n",
    "\n",
    "Оказывается, что распределение меток на трейне и тесте разное, хотелось бы обучаться на трейне, но как то оптимизировать функцию ошибки на тесте. Сделаем разумное предположение:\n",
    " - трейн и тест отличаются только распределением классов, но не данных в класах:\n",
    "  - $\\forall i: p\\left(X \\mid y = i\\right) = p\\left(X' \\mid y' = i\\right)$, где $\\left(X, y\\right) \\sim p_{\\text{train}}$, $\\left(X', y'\\right) \\sim p_{\\text{test}}$\n",
    "\n",
    "Обозначим за $p$ лучшую модель на тренировочном наборе данных, тогда:\n",
    "\n",
    "$$\\large\\begin{array}{rcl}\n",
    "p = P\\left(y = 1 \\mid x\\right) &=& \\frac{P\\left(x \\mid y = 1\\right)P\\left(y = 1\\right)}{P\\left(x \\mid y = 1\\right)P\\left(y = 1\\right) + P\\left(x \\mid y = 0\\right)P\\left(y = 0\\right)} \\\\\n",
    "&=& \\frac{a p_1}{a p_1 + b p_0} \\Rightarrow \\\\\n",
    "b p_0 &=& \\frac{a p_1}{p} - a p_1 = \\frac{a p_1 - a p_1 p}{p}\n",
    "\\end{array}$$\n",
    "\n",
    "По предположению, распределение меток на тесте просто скошено, т.е. $P\\left(y' = i\\right) = \\gamma_i P\\left(y = 1\\right)$, тогда:\n",
    "\n",
    "$$\\large\\begin{array}{rcl}\n",
    "P\\left(y' = 1 \\mid x\\right) &=& \\frac{P\\left(x \\mid y' = 1\\right)P\\left(y' = 1\\right)}{P\\left(x \\mid y' = 1\\right)P\\left(y' = 1\\right) + P\\left(x \\mid y' = 0\\right)P\\left(y' = 0\\right)} \\\\\n",
    "&=& \\frac{a\\gamma_1p_1}{a\\gamma_1p_1 + b\\gamma_0p_0} \\\\\n",
    "&=& \\frac{a\\gamma_1p_1}{a\\gamma_1p_1 + \\gamma_0\\frac{a p_1 - a p_1 p}{p}} \\\\\n",
    "&=& \\frac{a\\gamma_1p_1p}{a\\gamma_1p_1p + \\gamma_0a p_1 - \\gamma_0a p_1 p} \\\\\n",
    "&=& \\frac{\\gamma_1 p}{\\gamma_1 p + \\gamma_0 - \\gamma_0 p} \\\\\n",
    "&=& \\frac{\\gamma_1 p}{\\gamma_1 p + \\gamma_0 \\left(1 - p\\right)} \\\\\n",
    "\\end{array}$$\n",
    "\n",
    "Получается, что прогноз модели, обученной на тренировочном наборе необходимо перед посылкой преобразовать, используя функцию:\n",
    "$$\\large f\\left(x\\right) = \\frac{\\gamma_1 x}{\\gamma_1 x + \\gamma_0 \\left(1 - x\\right)}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = df[df['is_duplicate'] >= 0]['is_duplicate'].value_counts(normalize=True).to_dict()\n",
    "print('P(y = 0) =', d[0])\n",
    "print('P(y = 1) =', d[1])\n",
    "print('P(y\\' = 0) =', r0)\n",
    "print('P(y\\' = 1) =', r1)\n",
    "gamma_0 = r0/d[0]\n",
    "gamma_1 = r1/d[1]\n",
    "print('gamma_0 =', gamma_0)\n",
    "print('gamma_1 =', gamma_1)\n",
    "\n",
    "def link_function(x):\n",
    "    return gamma_1*x/(gamma_1*x + gamma_0*(1 - x))\n",
    "\n",
    "support = np.linspace(0, 1, 1000)\n",
    "values = link_function(support)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(support, values)\n",
    "ax.set_title('Link transformation', fontsize=20)\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('f(x)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Извлечение признаков\n",
    "\n",
    "<img src=\"./../images/buben2.jpg\" width=\"50%\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Длина вопроса\n",
    "<br />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df['len1'] = df['question1'].str.len().astype(np.float32)\n",
    "df['len2'] = df['question2'].str.len().astype(np.float32)\n",
    "df['abs_diff_len1_len2'] = np.abs(df['len1'] - df['len2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_real_feature(fname):\n",
    "    fig = plt.figure()\n",
    "    ax1 = plt.subplot2grid((3, 2), (0, 0), colspan=2)\n",
    "    ax2 = plt.subplot2grid((3, 2), (1, 0), colspan=2)\n",
    "    ax3 = plt.subplot2grid((3, 2), (2, 0))\n",
    "    ax4 = plt.subplot2grid((3, 2), (2, 1))\n",
    "    ax1.set_title('Distribution of %s' % fname, fontsize=20)\n",
    "    sns.distplot(df.loc[ix_train][fname], \n",
    "                 bins=50, \n",
    "                 ax=ax1)    \n",
    "    sns.distplot(df.loc[ix_is_dup][fname], \n",
    "                 bins=50, \n",
    "                 ax=ax2,\n",
    "                 label='is dup')    \n",
    "    sns.distplot(df.loc[ix_not_dup][fname], \n",
    "                 bins=50, \n",
    "                 ax=ax2,\n",
    "                 label='not dup')\n",
    "    ax2.legend(loc='upper right', prop={'size': 18})\n",
    "    sns.boxplot(y=fname, \n",
    "                x='is_duplicate', \n",
    "                data=df.loc[ix_train], \n",
    "                ax=ax3)\n",
    "    sns.violinplot(y=fname, \n",
    "                   x='is_duplicate', \n",
    "                   data=df.loc[ix_train], \n",
    "                   ax=ax4)\n",
    "    plt.show()\n",
    "    \n",
    "plot_real_feature('abs_diff_len1_len2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    404290.000000\n",
      "mean         20.158201\n",
      "std          25.584436\n",
      "min           0.000000\n",
      "25%           4.000000\n",
      "50%          12.000000\n",
      "75%          26.000000\n",
      "max        1080.000000\n",
      "Name: abs_diff_len1_len2, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(df.loc[ix_train]['abs_diff_len1_len2'].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Максимум среди дублей:        196.0\n",
      "Максимум среди не дублей:      1080.0\n",
      "Количество строк больше прога:  394\n",
      "Стандартное отклонение в дублях: 14.382099\n",
      "Новое значение:               224.76419830322266\n"
     ]
    }
   ],
   "source": [
    "max_in_dup = df.loc[ix_is_dup]['abs_diff_len1_len2'].max()\n",
    "print ('Максимум среди дублей:       ', max_in_dup)\n",
    "max_in_not_dups = df.loc[ix_not_dup]['abs_diff_len1_len2'].max()\n",
    "print ('Максимум среди не дублей:     ', max_in_not_dups)\n",
    "print ('Количество строк больше прога: ', (df.loc[ix_train]['abs_diff_len1_len2'] > max_in_dup).sum())\n",
    "std_in_dups = df.loc[ix_is_dup]['abs_diff_len1_len2'].std()\n",
    "print ('Стандартное отклонение в дублях:', std_in_dups)\n",
    "replace_value = max_in_dup + 2*std_in_dups\n",
    "print ('Новое значение:              ', replace_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['abs_diff_len1_len2'] = df['abs_diff_len1_len2'].apply(lambda x: x if x < replace_value else replace_value)\n",
    "plot_real_feature('abs_diff_len1_len2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['log_abs_diff_len1_len2'] = np.log(df['abs_diff_len1_len2'] + 1)\n",
    "plot_real_feature('log_abs_diff_len1_len2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['ratio_len1_len2'] = df['len1'].apply(lambda x: x if x > 0.0 else 1.0)/\\\n",
    "                        df['len2'].apply(lambda x: x if x > 0.0 else 1.0)\n",
    "plot_real_feature('ratio_len1_len2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    404290.000000\n",
      "mean          1.110796\n",
      "std           0.613775\n",
      "min           0.006711\n",
      "25%           0.793651\n",
      "50%           1.000000\n",
      "75%           1.271186\n",
      "max         117.000000\n",
      "Name: ratio_len1_len2, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print (df.loc[ix_train]['ratio_len1_len2'].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Максимум среди дублей:         6.666666666666667\n",
      "Максимум среди не дублей:       117.0\n",
      "Количество строк больше порога:  152\n",
      "Стандартное отклонение в дублях:  0.37610604511461654\n",
      "Новое значение:                7.4188787568959\n"
     ]
    }
   ],
   "source": [
    "max_in_dup = df.loc[ix_is_dup]['ratio_len1_len2'].max()\n",
    "print ('Максимум среди дублей:        ', max_in_dup)\n",
    "max_in_not_dups = df.loc[ix_not_dup]['ratio_len1_len2'].max()\n",
    "print ('Максимум среди не дублей:      ', max_in_not_dups)\n",
    "print ('Количество строк больше порога: ', (df.loc[ix_train]['ratio_len1_len2'] > max_in_dup).sum())\n",
    "std_in_dups = df.loc[ix_is_dup]['ratio_len1_len2'].std()\n",
    "print ('Стандартное отклонение в дублях: ', std_in_dups)\n",
    "replace_value = max_in_dup + 2*std_in_dups\n",
    "print ('Новое значение:               ', replace_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HDmh1nkS7jZF9k+WErBAiIsJVo98AnBWmZces9UXVzF1XxPnjB5DiaHsYoQP6p7IyvxKvT4YsFkKEV1gSvdZ6FtBrzjS6DSit8/HMN5tJtFs5af9cSut8+Nq4+HXsgD64vX50oQxZLIQILzkZGwI1Hh/Pfr2JBeuLGZ+XxuLNpcxbU4CnjWEOxvZPBeCXHdJOL4QIL0n0IVDt9vLJqgIyk+I5fGh6u/NbLBbsCXH07+Ng4cYSSut8lNb5cMvwN0KIMJBEHwL/+XozVW4vp4zJIc7W/lda6/Uzb00BQzISWbK1jA9+2cm8NQXUeHwRiFYI0duELdFrrTdrrQ8N1/JjxeJtZXyyajcTh6TTv09wnYz265eKAazKl/70QojwkRp9FxiGwRMLN5GdHM+RrVwc1ZbMpHj6pSawYpckeiFE+Eii74J564pYmV/JJRMHYe9Ak01LxvRLZXelm8Iqd4ijE0IIkyT6TvL6DZ76ejPDMp1MHdXymDYdsW9uMhYLUqsXQoSNJPpOen9FPltLa7n+yKHYrJb2P9CKpPg4hmU6WbmrEo9cPCWECANJ9J3g8vj476ItjO2fyqThwbfNNzVhUBqVbi+zf9kZguiEEKIxSfSd8PqSHRRV1zH9qKGN7h7VWcMykxiRlcSrP2yjSNrqhRAhJok+CG4DtpS7ePGHbRw6NJ3BfZPbHeqgo44dmYXHZ/Dk15u7vjAhhGhAEn0Qajw+Hpqzlpo6H/vlpjBvTUG7Qx10VEZSPGeP689HK3ezYpeMaimECB1J9EEoqHSzeFs5+/VPITs5IeTLv+DgPLKS4nl47gb8hoyHIIQIDUn0QXj5+60AHDUsMyzLd8bHMf2ooazMr+TjVbvDUoYQoveRRN9BG4qqmbOmgIMG9qFPoj0sZVgsFg4dkcmonGT+uXAT26vcMtiZEKLLJNF30NNfb8Zht3HYkK53p2xNrdfPAl3IxMHplNZ4eOATLYOdCSG6TBJ9ByzdXs7CDcWcd1AezviW7wMbSv37ODgwrw8/bC2TAc+EEF0mib4dPr/Bw/M20Dc5njPH9otYuVNVFgPTHHy0cjerJdkLIbpAEn07/rdsF7qgit9NHo7DHv7afL04q5WzxvYnOSGOuz9cza4KV8TKFkL0LJLo21BaU8e/vt7MhEFpTB2ZFfHynfE2zj2wP16/n9/PXkGV2xvxGIQQ3Z8k+jY8+dUmajw+bjtmREiGOuiMzKR47jppFJuLa/jTR6vxhuDiLCFE7yKJvgVuAxZsKuH9Fbs5e1x/0lISQjbUQWeMH5TO9MnDWbSplLs/1RS5vNLtUgjRYXHRDiAWldV6uP9TTVpiHIPSHMxbUwDAYftkRyWeWq8fZ5yVI4dl8PnqAraW1HDafrlM3TeHhAj0AhJCdG+S6Fvw+k/bKKnxcN74/p2+c1Q4HDU8k3iblbnrinB7d3L4iEzSJdELIdoRO1ksRuiCKt5cvIMx/VIYlpkU7XCamTgknZP37cvmkhpunrWCfOmNI4RohyT6BspqPPzhvZWkJdqj0sumo8YO6MO54/qTX+7i8teW8uPW0miHJISIYZLoA7x+gzs+XEVxdR13/2oUzvjYbtUalpXEY9P2J9Fu5fq3l3PPp5qyGk+0wxJCxCBJ9AH/XLCRn7aVc+dxI1E5KdEOp0OGZSXz9AXjuGBCHp+uLuDsF37k7WW7cEkXTCFEA5LogY9W7ub1JTs4f/wAfjUmJ9rhdFit18+i9cUMSU/kskMGkpwQx0Nz1jH97WUs3FCMV242LoSgl/e6cRuwdHsZ981Zy9i8Plxy6KCo9pfvir4pCVxycB4/by/nuy1l3PLuStIT7Zwwui+n7JvDyL5JUbvoSwgRXb060W8oquaO91bhtNuYPDyDr9YVAdHrL99VFouF8QPT+N2x+/DDllLmrC5g1tKdvLFkB0MznZw0JodTR/clKwx3xxJCxK5em+i3l9Xyh9kr8Pj8XDghL+ZPvgbDY4DL7eWoYRlMGNiH1fmVLN9VydMLN/HMV5sYN6APR4/I5PAhGQzOSJSavhA9XM/JbkHYWlrLdW/9Qq3XzwUHDSAnpefWcBPtNsYPTGP8wDRG5KTwpS5g0cYSHpu/kcfYSKojjv36pzKufyoH9E9ln+wkUh3huYOWECI6el2i/3l7OX94byUWi4WHztiPrcXV0Q4pYrJTHQxOS2Tw+AGU1NSxtaSWHeUutpXWsmhjyZ75+jjiGJSeSF5aIgPTExkUeBzQx0GqI06OAIToZnpNovcbBu8vz+ehuevpn+pgxln7keSM71WJvqEMZzwZznjG5fXhmNE5lNfUsTq/kq2ltewoq2VnuYvF28v5ZHVBo8857TZyUhPol5pAboqD3NQEclMT6JucQHJCHCmBv6QEG1bZIQgRE8KS6JVSVuBpYCzgBq7SWq8PR1ntcXv9zN1QzHOLtrClpIaxeX34y0mjSHLEdcveNeFQ6/WzeIt5dW2W006W087Y/qkcMzoHl8fLznIXO8pc7K5wUVDpprCqjvwKFyt2VVLhanmMfAuQlGAjJSFuzw4gOSEOR5wVu82C3WYN/FmItzWeFm+zEBd4tFut2OMaPjcf421W4pp8tuFzm1V2MkLUC1eN/gzAobU+TCl1KPAIcHqoC1m+s4LF28owAMMwa+2GAS6vH5fHx+aSGn7ZWYHb6yczyc7p++cyOieZnzabzRTdtXdNpNR6/Xy7vnjP64xEOxmJdi4/Iptv1xUCUOf1U+HyUuX2sk9uClVuL9VuL9VuH1V1XmrrfHve315Wi9vnx+sz8PgMPH4/nsDrUI+zb7XQaEcSZzV3HnFWC1YL2KwWrBZL4LUFm9WCrcH0+keLxdxp7XluMT9vwXxuPprvWxu+v2c+S8vvB6Y31XRS83kszd63NH1eH1ej6W3s+Fp5q61dZVsHa22V1dmDvLZjaaO8UMfRibLaKm9wupMjhmV0LpggWAwj9NVapdSjwA9a6zcCr3dorQe08ZFCYEvIAxFCiJ5rH+Bb4MT2ZgxXjT4VKG/w2qeUitNat3YvPKlaCyFEmIRrCIQKoOGAMdY2krwQQogwClei/wY4GSDQRr88TOUIIYRoR7iabmYDxymlFmGeo7g8TOUIIYRoR1hOxgohhIgdMkyxEEL0cJLohRCih+sRQyDE0pW4bVFKTQT+rrWeHO1YGlJK2YHngSFAAvB/Wuv3oxpUA0opG/BfQAEGcK3WekV0o2pOKdUXWAwcp7VeE+14GlJKLcHsDQewSWsdU+fNlFJ3AKcB8cDTWuvnohxSI0qpy4DLAi8dwDggV2tdFq2YgtEjEj0RuhK3K5RStwEXA7E4uM5FQLHW+mKlVAawFIiZRA+cCqC1PkIpNRm4j9j7/9qBfwO10Y6lKaWUA7DEWgWjXuB/ejhwBOAEbo1qQC3QWs8EZgIopZ4Cnu8uSR56TtPNkcCnAFrr74AJ0Q2nRRuAs6IdRCveBu4KPLcAMXXNg9b6XeDqwMvBQCz+wB4GngF2RjuQFowFnEqpz5VScwOVoVhyAmYX7NnAB8CH0Q2ndUqpCcAYrfV/oh1LMHpKom/xStxoBdMSrfUswBPtOFqita7SWlcqpVKAd4A/RzumprTWXqXUi8ATwKvRjqehwGF9odb6s2jH0ooazB3RCcC1wKsx9vvIwqycTWNvfLE6Kt2dwL3RDiJYPSXRy5W4XaSUGgjMA17WWr8W7XhaorW+FBgJ/FcplRTteBq4AvO6kfmYbbcvKaVyoxtSI2uBV7TWhtZ6LVAM9ItyTA0VA59preu01hpwEYPDoiil0gCltZ4X7ViCFRP96P1+v+GTMYOFEKLD7HZbER3cIcbE4ZvPZ1BWVhPtMIQQotvIzk7p8Ii/PaXpJubNXVfEwg3F7c8ohBAhJok+Qh6Zu57b3l/F4m2x2GFECNGTSaKPgKIqNwVVdWAY3P7+KraXxVxXayFEDyaJPgJW5lcBcNcJCoAH5qyLZjhCiF5GEn0ErMqvwGaBg4amc9Q+WazaXUmJ24tbOhoJISJAEn0ErMyvZGhWEovWF1Pr9lLl9vHx8nxqPL5ohyaE6AUk0YeZ3zBYlV+FykkGIMMZD0BJTV00wxJC9CIx0Y++J9tWWkul24vKMS/cTXfaASipicnREEQMq62tpqqqHJ9Ptp2eymazk5zch8TE0F74LYk+zFbmVwKgcpLZXFhNWqIdiwVKpUYvguDx1FFZWUpaWhZ2ewIWS6wOBSM6yzAMPB43ZWVFxMXZsdvjQ7ZsaboJs1X5lSTarQxKdwJgs1pIc9ilRi+CUllZRnJyH+LjHZLkeyiLxUJ8vIOkpD5UVYX2ehtJ9GG2Mr+SUTkp2Kx7f5wZSXZKJdGLIHi9dSQkJEY7DBEBDkciHk9oj/gl0YdRaU0dq/IrOXBAaqPp6c54SqrriIUB5UT34Pf7sFpt0Q5DRIDVasPvD22PPEn0YTRvXRF+A6aqxgPMZTjtePwGxdXSTi86Tppseodw/J8l0YfRnLVFDE5PZERW4zPoGYGeNzvKXNEISwjRy0iiD5Pi6jqWbCtjqsputoeu70svY94IISJBEn2YNG22sdUW43QXAJDqiMNmtbBDEr0QjUyffjXnnHNq2JZ/3333cOSRsXhL6fCSfvRh4Dbg0zUFDEpPJCMlgdI6H/3n38wZhat5c8IsfNYE0hPt0nQjQsJtEFPDaTjtNhI62cx86aVXUFsrv4tQk0QfBtvLavllRwVHDstgvi7E6vdw5a7vsPpcjN41mxUDzifDaZemGxESNR4f89YURDuMPaaM6ktCfOd6CB188KEhjkaANN2ExdeBO0mNCoxvk125CqvPhTsuhQO3vUCcr5ac1AS2l9ZSXiv96YUQ4SWJPgwWrCsiKyme7OQE9s00OKTuWwC2H3QHTk8JJ5a9ytFDEjGAJdvLoxusEDGkaRt9XV0djz/+CNOmnc6UKYdx1lm/4pFH/k5FRUW7y1qzZjW///1vOe64SZxxxkm8/PILza5daa3Nvun0++67h/POO4MVK5Zx5ZUXc8wxR3DhhWfz7rvvdGFtI0eabkKssMrNip1msw1AklFL4pYvqXPmsqO4knTnMPqueZGxx59OQpyVxdvKmLJPVpSjFiI2PfbYQ8yZ8ynTpl3AgAED2LhxA7NmvcX27Vt57LGnWv3cxo0buOGGq0lJSeWyy67E4/HwxhuvUFfX+SPo8vJybrnlBg499AhOPvkUFiyYx8MPP0hlZSUXX3x5p5cbCZLoQ2zu2iIMYFRgtEqLr45U13Zqcg8BYFvaoYzd+RoZJUsZ028cP26Ve8gK0ZrPP/+EX/3qNK655rd7piUmOvn++2+pqanB6XS2+Lnnn/83YOFf/3qOnJxcAKZMmcrll1/Y6ViqqiqZNu0CbrrpFgDOOOMcbrrpOmbOfJbTTz+b1NTUdpYQPdJ0E2JfrC1kaKaTrGSzr7yzbDU2w4s7dRgAlY7++LGSVLKCsXl92FhcI2PTC9GK7Owc5s6dw8cff0BlpTkS7G9+cx3PPvtSq0ne7/fz/fffcdhhR+xJ8gCDBw/hkEO6drL34osv2/PcZrMxbdr5uN1ufvrphy4tN9wk0YfQznIXS3dUMKlBU0xK0VIMwJ06BAC/1U5VQg5JpSsZl9cHgMXbpJ1eiJbceusf8fsN7r//Xk45ZSq//e1vePPNV6mqqmr1M+Xl5dTW1jBgQF6z9wYNGtLpWFJT+5CRkdloWl7eIADy83d2ermRIIk+hN5bkY8FOG5U3z3TUop+pjo+B8O+t/ZR4cgjqWwN+2TG47TbWLxNmm+EaMmECYcwa9aH3HPPfRx77PFs2bKZJ554jEsuOY/S0tIWP1N/Jbrb3bw/fkcHEmxpUDG7vXlLd/18Vmtsp9KwRaeUmqiUmh+u5ccar9/ggxX5HD40g74pCQDE+WpJKllJWeKgRvNWOAZg9blJKtOMy0vlJ2mnF6KZuro6Vq5cQVVVJVOnnsBf/vI33n//M66//iYKCnbz5Zeftfi5Pn36kJSUxPbt25q9t3Pn9kav6xN0XV3j5tPi4uJmny0pKaGmpqbRtG3bzDLqa/axKiyJXil1G/As4AjH8mPRNxtLKKyq48wD9rYJjtv2IlZ/HYXJoxrNW+kYAEBoG8IfAAAgAElEQVTi7sVMHJzOltJauXhKiCYqKsq59trLefnlF/ZMs1qtjB69b+B5yxdlWSwWJk2awvfff8vGjRv2TN+1ayfffvtNo3nrm2LWrVu7Z1pBwW5WrFjWbLmGYfC//72157XX6+Xtt18jOTmZCRMO6cQaRk64et1sAM4CXg7T8mPOu8t3kZUUzxHDMqn0+klx7WDs9pcpyZtKpSOPhgMVu+2p1DmySSxYwriDLgDgozUFnDs+r0uXjwvRk2RlZXP88Scye/Y7uFwu9tvvAMrLy/nf/94iIyOTY445rtXPXnXVtXz77dfccMPVnHvuhdhsNt55502cTmej2vuxxx7PK6/M5J577uTccy+krs7NrFlvkZ3dl23btjZb7syZz5Kfv4uhQ4cxd+4XLF++jD/+8S4cjtiu04Yl0WutZymlhoRj2bEov8LFok0lXHbIQOICd5I6bOMMDIuNHfteCxsXN/tMdcYYknYvZs2uCnJTEvh4xW6ynfFdunxc9E5Ou40pDc4LRZvTHrrt97bb/kT//nl88cVnfPHF5yQmOjjooEO4+urrSUtLa/VzOTm5PP30czz99OO89tpL2O3xnHrqGQCNjhBGjNiHv/71AWbOfJann36cvn1zuOiiy3C5XDz99OPNlvvoo0/y8MMP8PHHHzJkyFDuu+8fHH30lJCtb7hIP/oQ+GDlbvwGnLa/2WyTtOULBhTP5/sh12NPzG7xM1UZ+5G+cz5OdyEj+yazcEMxFS4ZDkEEL8FCj6kcPPnkfxq9TkhwcOWV13DlldcEvaxBgwbz4IOPNpvesE8+mP3rp0yZ2my+Cy+8uNm0Aw4Yx0svvRl0LNEW26eKuwGf3+D95flMHJzGgD6J2Ld9Rb8vr6fYOYLlA1q/OKM6fQwAOZXL94yJs7agOiIxCyF6F0n0XeA2YO6GYvIr3UwdnYN74zxSP7qMutShfLj/0/isCa1+tjZtH/y2ePqXLSYzKZ6spHh0Qet9g4UQorPClui11pu11j16zNEaj4+XvttKot3GUSXv0P/Ty3El5VFx4hPsn5vAwRk1ZDlb7rdrWO1UDDqefQo+wu6tQvVNZltpLYVV7givhRCip5MafReU1tSxobCcR5NfYdSyByhLHMTPmadRt+VHqlZ+StXKT7Eb3lY/X7j/1ST4qhmdP5sDBqSCBWYvje0r7ITojf70p3v4+uufoh1Gp0mi74IvVhdwv+2/nFT7AbtHnM+KftPw2TrWzSoryUZqZj8qsw7koF2vc3y/Go4YmMjHK/Opcre+cxBCiGBJou8kwzAY8svDTItbyE+DrmbHmOvA0vGv0+534dFz2Gzfh3hXIc7vH+N4+zJq6vzMXrYrjJELIXobSfSdVLLwSX7tm82C1NNYPOiqTi+n1DmM6vhs8sq+Z4ijhoMHJPLGkh14fP4QRiuE6M0k0QfJQTXJmz9g5Ip/MMc4GPuRN3JwZm2rJ13bZbGwJeNIkuqKGFzyFZccmEFBVR2fxdA9QIUQ3Zsk+iDZStYR/9ktbPD3Y27mRXjXftnuSdf2FCWPIj/lAAaVLmKSfTXDs5y88tP2Do+0J4QQbZFEHwyfm4Q5f8Dn83O152ZOzAvdSdP12cdRa88gZcFdXHlAMhuKali0ueVhWIUQIhiS6IOQtOh+bEWruc96DY6UTEalhu7OUH5rPKtzT8fiLuf09X9kgNPghR+2UVrnwy0VeyFEF0ii76D4zV/iXPYc+cOm8WL1RM7OC/1VrNUJuZQfdS/OgsX8M+Fplm8v5aVvt1DjaX4TBCGE6ChJ9B1grd5Nype/x5u5L//kQhxWPyf3C8+4NO6hU1k07GYOqv2Gvztm8vHKXbgk0QvRaaWlJdTWdu5+D9OnX80555wa4ogiT0avbI/PQ8pn14O3hvVHzuC9/5VwbE4NNoufcHSATIwzSNz/NPJt+Uxb9yo+j5e3Fjm5/biRuEgKQ4miu3NQjdUTO+Mk+e3JMbOtfvvtN9x775954YVXSUxMjHY4USOJvh1Ji/5G/K7v2TXln8xYZqXa42ecYzc6v4ZhfVNCXp7VW0vVyk9ZawzEnX4E55fO593VdSwZdh/7Dt8n5OWJ7s/qqcK/dk60w9jDOvI4sMdGol+1yrwVYW8nTTetcFBNyqoXcC57Hs/+v8Y38FC+31TEmGw7I5017S+gqywWtmROYl36ZM6wLSLp0+lUlBaGv1whRI8jNfpWxO34noQF9+LtMxRX+r689MVPlLrSePCYPlia32EsbHZlHoYvOZcDtr5D8ZunYD3vNfzpwyMXgBARdM45pzJx4mEccMA4Xn55Jjt3bqdv3xymTbuAs88+t9G8v/zyM88//19WrVoOwOjRY7jiiqsZN248APfddw+ffPIhANOmnca4ceOb3dikoR9//J7nnvs369evJSMjk0suubzZPNOnX01+/i7eeeeDNqdPn341Nlsc06adzzPPPMHOnTsZPHgwl156JZMnH9v5L6iTpEbfAmt1Pgmf34JhT8Y15tcUeey8vCWVg1PLOaCvPeLxpA8eyydj/kG8twLnm6dg3/5N+x8Sopv67rtFzJjxMFOmHMsNN9yMw5HIY489xLfffr1nnq+/XsANN1zD7t35XHrplVx66ZXs3p3PTTddx9dfLwDg9NPPYtIk8zZ/N954M5deekWrZf744/fceuuNVFVV8ZvfXMexxx7PjBkPo/XqTq/H5s0bueuu2xk3bjzXXXcDFouVP//5dj7//NNOL7OzpEbflLeW1I+vxOKppmbs1RjxyTyzqg91fgvTcvKBKLSTW+CYI47gobJHuXj7PYx4/0Lch9+Gd+yluCzJkY9HiDAqKNjNCy+8xogR5m9t0qTJnHHGSXz++accdtiReL1eHn30IbKysnnuuZdISjJ/A6effjaXXHIejzzydw499Aj22+8Ahg8fwcKF8zjqqMn069e/1TKfeeZJMjOz+Pe/n9+zvIMPnsiNN15Lnz6t35u2LcXFRdx4482ce655p7nTTjuDSy+9gKeffpypU4/Hao1cPVtq9A0ZBilzbyWuYBnuKX/Dn5zLzlobs7Ylc1r/KnITQneBVDA8PoOqygqmJG7kLvsfWODdj8RvHiDxs5uwuCuiEpMQ4TJo0OA9SR4gMzOLjIwMSkqKAVi7dg0FBbs5++xz9yRlgJSUFM4661wKCwtYs2ZVh8srLS1B69VMnXpCo+WNHz+B4V3oAJGcnMyZZ07b8zohwcEZZ5xNUVFhl44UOkMSfQOJS57Cse49qg+9Hd+QyQD8Z0MfLMDlQ8ujGhtAnAWuHFTI3+Ou5yHPuVg3zCH9zROwb1sY7dCECJm0tPRm0+z2eHw+83qSXbvMm/MMGjS42XxDhgwBID+/40N91887YEBes/cGDx7S4eU01b9/HnZ746bevLxBAOzaFdmhyCXRB8Rv/Izk7x7Etc8Z1I437xK/tTqOd3ckMW1gFTmO2LhoyWHzc+vQLWzMnso57r9QVOsj7f0LSf3oCqzlm6MdnhBdZrFY2ny/rcH+/H7zvaYJtiPlud3Nb+Pp93fsapmW5msphvr5bLbIpt5en+gdVJOU/zWpc6bjyx6D76g/4PQWEIeHGWvTiLMYXDUs+rX5huKtBveMKWbyYYdzVNUDzHRchH3712S8NoU+3/wZR93uaIcoRNjk5ppt7Vu2bG723tatWwDo2zcnqOVZLBa2b2/enW7nzh2NXttsNjye5k249c1KDe3atbPZTqm+jPqafaT0+kRvrdiG48NrMax2akecjn/jV/jXzuHztWXM2e3kmuEVZDti7yYgFitcNc7BXw8o5cGKEznO9SA68UDil84k+ZXjSPrqbuJ2/gBG7MUuRFeMGjWazMwsZs9+h+rqvVcEV1dXMXv222RmZqHUaMBMzNB2zTwtLY2xYw/k888/aZSwV6xYxtq1axrNm5GRSWlpKUVFe69pWbNmNdu3b2u23JKSYubO3XshW21tLbNnzyIvbxDDh48Icq27pnf3uvG5ccz5AxZPFTXjrsVISAWgyG3lnu/KUCluzhtYTo2XsAx30BUen0F5TR39/bu5e1gZL+/qz4nFv+OkxDX82fkF/Ve+gnPZc/icOdQNOxH38JPx9J8I1t79LxfdX1xcHL/73a3cffedXHnlJZx66ukAfPDBexQXF/G3v/19T4+WtDSzx8zrr7/MoYcezpFHHt3iMqdP/z2//e1VXHPN5Zx55jRcrlreeuu1PZ+vN3XqCcyZ8ym33HIjZ555NiUlJcya9SZ5eYPwej3N4rz//nvReg1ZWdl89NH7FBbu5qGHZoT6K2lX7/3VG35Svvgdtvyfqd33Avyp5okYnwH3rMigzOXn98O2sqHABRCW4Q5CZYDDze1DNrG4MpWPiodxxI7pjO7j4U8j1nKI+1scq98gccWL+BMzcA89kbrBx+AZcPieHZvo3vz2ZHPYgRjht4e/y++UKVNJSUll5sxneeGF/xIXF8e+++7HHXfcxdixB+6Z79hjT2D+/Ll8/PEH/Pzz4lYT/ahRo3nyyf/wzDNP8sIL/yElJZUrrriaNWtWs2zZ0j3zHXHEUdx88+289dbrPP74IwwcOIhbbrmDpUsXs2jR142WmZWVzY033sJTT82guLgIpUYzY8bTey7oiqTemegNg6Rv/opj/Qe4J96EN7Ff/WT+b2U6Cwqd3HRwMoNqXVEOtOMsFpiQWsEFI+EH23ie+mYXF/0yhlyH4szc0zg3aQl55JOw7j0SV72GYbHhzTmQuoFH4ck7Am/6PhiODHNBoltxkRQzY8t0VdMrTtuaPmHCIUyYcEiby0tJSWHGjKc7VPbo0WN4/PF/tTvfWWdN46yzpjWadswxU7n55tubzTtp0mQmTZrcofLDqfclesPA+cMjOH95lpoDrsQ44GJY9wV+Ax7VabyzPYVLhpRz/pi+rPsp2sEGz+c3OCTPRtpgzdLKFL4oyeTfm7P5FyeSl2rn6MG/5ZzMLezrXkrcju9x/jgDy4+PAWZNzJs9Bk+/iXhzDsSXOghf6kCwO6O8VkKIruhdid7wk/zVXSQufxHXqHOpPvJunN5CqrwW7liWyfwCJ+cNquT64WV095s6WSxwYGolB6ZWkpWRxvwCJz+6BjBrVSWv+rPITpjMhPTDOGhgKWMz/fT15ZPt2UFC8UrsS57CYuztTupPzMSXMhB/ygB8yf3wJ/XDn9xv7/OkvmCLj+LaCiHaEpZEr5SyAk8DYwE3cJXWen04yuqoxIq1OBbeS9yWBXgOuAjfxJtwegv5dks593yTyy5XHLeqEs7Jq8ToYa0XqXE+9ovbyZmTR7Pih5/4uSIV7clkcWkCn+TnwToAs7vXQOcpjEisZH/bNlSGhcG2Yvr6C0iq3UVC0Urit8zF4m18EwcDC35nNv7kfviTcvE70jAS0vA70jES+gSem4+G3YkRl4gR58CwOSDOAVZb5L8UIXqRcNXozwAcWuvDlFKHAo8Ap4eprNb5PMQVrSRhw4ckLnseDD+u4b9id/IBLFrwLZ/uSmJBYSI58X5uH7KRUfYa1u42T7z21NSTYDU4NK2cC/v62VhQSbk3DueIw1n6y1Lq4vuwvtJOviuJN6rHUFgY1+zIxmH1MSDRx/D4UobYyxhgK6afpZQsfzHprhJSK9eS6Ksk3ltJnL/5BSgtMaz2PcmfwA7AiHNgxCWarwN/2BICOwjzEasdwxYfeLSD1d7oufkYH3iMw7DGg82OYbWb/VMtNrDaMCy2wHNzmmGN2/u+xRp439JompzLEG1pa5TMaAhXoj8S+BRAa/2dUmpCOApxrHqN+I2fmc0Mhh/8PjB8WLwufO5qLBXbSDDME6rz4o/mef9pLFvbl/KVZhrPiPdx4yGpjK1ahN3a3RtrOqdPnJd9suzY+1QwrK/BxoK9N2nIG3sMW9cvo6DWRrnHRpnHSpnHSnViP4qLvXzn7kOFZxjlXivVXit1/sbJL4E6UqkmzVJNGlWkWapIxI3DUoeDOhIxHx2WOhx1dSRQh9PiwWkxpyVaKnFQjAMPDuqIx0MCbuIDr21R7PTqx2IeyWDFjxUDC4bF2sI0C2B+Lwbm0Q9YAjtQS+A1YLE0eX/vd2lgwTj2CQxrTStHmw3nbfv9ludreafV+i+iZ+3kbFYL9nBeqWoYUJ+jLBbAuqfiYMQl4k/uF76yAyxtXU7cWUqpZ4FZWutPAq+3AsO01t5WPlIIbAl5IEL0ECtWrNy/f/8hrf1+RA+zc+fmuP32G7O8ndkGA9kdWV64avQVQMOO59Y2kjx0MFgheiuPx7PR6/WVtDcOjOj+DMPA4/FkACFrCQnX8co3wMkAgTb69vZMQog2WCyWMp/P27t6yfVSPp83zmKxlIZymeFK9LMBl1JqEfAY8PswlSNEr2AYxi8uV03PuCpKtMnlqkk2DOOXUC4zLDUErbUfuDYcyxaiN/L7ffdXVJR8FBcX70xIcNRIE07PYxgGbrfLWVFR4vf7fQ+EctlhORkrhAi9xYsXn2izxd1mGMZQelrXFwFgWCyWTT6f96GDDjoopDeWlUQvhBA9XK8fj14IIXq6HnMWPxaHXWhKKTUR+LvWenK0Y2lIKWUHngeGAAnA/2mt349qUA0opWzAfwGFeR3PtVrrFdGNqjmlVF9gMXCc1npNe/NHklJqCWa3Z4BNWuvLoxlPU0qpO4DTgHjgaa31c1EOaQ+l1GXAZYGXDmAckKu1LotWTMHqMYmeWBl2oRVKqduAi4HqaMfSgouAYq31xUqpDGApEDOJHjgVQGt9hFJqMnAfMfS/hT07y38Dte3NG2lKKQdgibUKRr3A//Rw4AjACdwa1YCa0FrPBGYCKKWeAp7vTkkeelbTTaNhFwjhxQYhsgE4K9pBtOJt4K7AcwsQU1dgaq3fBa4OvBwMxOKP7GHgGWBntANpwVjAqZT6XCk1N1ARiiUnYF5rMxv4APgwuuG0LDCUyxitdWwNZNMBPSnRpwIN7+LtU0rFzBGL1noW4Gl3xijQWldprSuVUinAO8Cfox1TU1prr1LqReAJ4NVox9NQ4NC+UGv9WbRjaUUN5o7oBMxuz6/G0m8DyMKsmE1jb3yx2KvoTuDeaAfRGT0p0Qc77IJoQCk1EJgHvKy1fi3a8bREa30pMBL4r1Iqli4eugI4Tik1H7P99iWlVG50Q2pkLfCK1trQWq8FioHwj6TVccXAZ1rrOq21BlzE2LAoSqk0QGmt50U7ls7oUPdKpdQpgEtr/UWDaadrrd8LRRB+v9/w+WKrm+fGjXvP4w4bFtk7tovQstttRbSQOMKxXQeS/bVa6zWxuF2LnqO17bol7R6+KaWeBtKAOKXU74GztNZu4CYgJIne5zMoK6sJxaJCYtq0U5tNe/vtlu9lKWJfdnZKs5FRe+N2LXqWlrbr1nSknW5/rfVRAEqpG4A3MXu4xGIbmhAdFZbtOlZ7tjS0ePGP9O2bw8CBg6IdioiQjrTR25VSCQBa6yeAdUqpf4Y3LCHCrtdu1w8++FcefPCv0Q5DRFBHEv3jwAqlVH1b0G1AInBU2KISIvx69XZdULA72iGICGo30WutXwf2B4oCrw2t9W+Ag8Mcm+hGSktL+Mtf/khpaUiH0Q4b2a5Fb9KhvrRaa5dSapxS6mrMS4DrXRGesER38847b7BmzSpmzXqDq666LtrhdIhs16K3COaiiZnAk8C28IQiuqvS0hLmzfsSwzCYN+8Lzj77fNLT06MdVkfNRLZr0cMFk+jztdbPhi0S0W29884bGIYfAL/f361q9ch2LdqxZs0qEhIcDB06LNqhdFowiX6zUuqPwM+YIwiitf48LFGJbuWrr+bj9ZoXIXu9XhYunNctEr1SKh94tzPbtVLqd5gjGP4xvFGKaLvrrttJSHDwyitvRzuUTgsm0SdgDhOrAq8NQBK94KijJjN37hy8Xi9xcXFMmjQl2iEFI6jtWimVCDwLHALMCnt0Iia43a5oh9AlHU70WuvLlVIjgRHAMmJzlD4RBeeccz5z55qjCFgsFs4++/woR9Rxge36FMyhj2uAnUqpPsCBwO1AHTAMeENrfR/mSdsXgTnAqOhELURwOjyomVJqOvAvzB/E2UCvuLhEtC89PYPcXHMMr9zcft3pRGz9dv0q5g0v3sAcFuG2wNuDMbf1Q+unaa1LpclSdDfBjF55PnAcUKa1fhyYGJ6QRHdTWlpCfn4+APn5u7pNX/qA+sOPXOBMzCaZAYFpy7XWXq11NTF4QxEhOiqYRG/FbL+sH47PHfpwRHf0zjtv4PPtPRk7a9YbUY4oKFZAA6sC49RsZO+NL2ToSdEjBJPoXwMWAiOUUh8D74YnJNHdLFw4j/rhrg3DYMGCbjVk92uAHThIKVWCeU+DZdENSYjQCuZk7JNKqS+B/cyXWn4MAjDb6Hft2ntuPiMjI4rRdJzWOhegle16LTC/6bwNXs+MTJRCdF1HxqN/gOaHsAcqpc7XWt8ZnrBEd7J7d+MBsurb62PFhg0b1Mknnzy/yeRizCabhjq1XQfGtt+36fQVK1YFFacQ4dKRGv2att5USiUEbtggeimLpe3X0TZ8+HDddJx4pdSlbX0mmO1aa319S9M9Hrm9lIgN7SZ6rfWL7czyCXBM04lKqSWY93EF2KS1vjz48ER3sM8+I1mzZvWe1yNHxn73ctmuRW8SijvBN6u/KaUcgKU73G1HdN3atY1bQLRe3cqc3Yps16LHCEWib+nwdCzgVEp9HijjTq31dyEoS8Qgv9/f5utuSrZr0WME070yGDXAw8AJwLXAq0qpUOxUhIgm2a5FtxSWphvMrmnrtdYGsFYpVQz0Q8b8Ft2HbNeixwhFjb6lPmRXAI8AKKX6A6nArhCUJUSkyHYteowu1+i11r9tYfJzwEyl1NeYbZ1XaK29XS1LiEiR7Vr0JB25YOr+1t5r7cISrXUdcGEX4hIirGS7Fr1JR2r0BcB1mMMTx9ilMEJ0mmzXotfoyAVTM5RSE4CdWusvIhCTEGEn27XoTTraRn8V5p11hOhJet12XT/KqOhdOpTotdYuoHvfNFGIJnrjdl1/E3fRu3S4141S6k7M26nVYLZpGlrr/uEKTIhI6G3btcfjiXYIIgqC6V55HtBfa10TrmCEiIJetV17vZLoe6NgLpjahNw3U/Q8vWq7lqab3imYGn08sFwptZzAgE9aa+lTLLq7XrVd19b2mn2aaCCYRP/3sEUhRPT0qu3a5epV555FQDBNN0uA44BLgUxgR1giEiKyetV2XVNTvee5dLXsPYJJ9M8DG4F9gHzMcT+E6O561XZdWVmx53nDpC96tmASfabW+nnAo7VeFORnhYhVvWq7Li4u2vO8qKiojTlFTxLURq2UGhV4zAPk9L3oEXrTdt0w0ZeUSKLvLYI5GXsT8AIwGngHuD4sEQkRWb1qu66oqGjxuWhZT+mO2uFEr7VeDhwWxliEiLjetl3v2LEdX3JfbNWF7NixPdrhxLzq6r3nMXw+HzabLYrRdF5HxqPfRcs3SqYnXyouerbeuF3X1tayecsmvNn7YjV8aL062iHFvPz8vTcQKyoqJCcnN4rRdF5Hhinu19b7SqnTtdbvhS6k0FmwYC5z584JybLuvvuOoOY/5pjjOProY0JStgi97rxdd9bPP/+E3+cDLNSlDmD1mhVUVJSTmton2qHFrA0b1jZ4vq7bJvpQ9DC4KQTLECLW9LjteuHC+WCxYqvajTd9KIbfzzfffBXtsGLa0qVLyEw0SIwzn3dXXb5nLDF8d56jjz6mU7XqadNObTbt3nsfCEVIovuI2e26M9asWcXixT/gj08CiwW/MwN/cl9mzXqLSZOmkJSUFO0QY05hYQFLly7hV4NclNdZWLRoIZdeeiVJScnRDi1ooUj0cnldDyFNXY30mO26vLyMx2Y8DI4U/HFOc6LFQu3AiVjXfMhTT83g1lvvwGrt0ZcQBMXtdvPPx/+BFYMUu59D+nr4Jt/Bk08+xq233tntTsrKf7YFb7/9QZuvheguCgsL+Otf76KktJTqYVPAsvdAxZ+cjSvvEH788TueemoGbrc7ipHGjvz8Xdx7zx1ovYacRC8/F8UzJMXHr/ep5qeffuCB+++htLQk2mEGpUc33YjgSFNXI916u66pqeGTTz7gf/97G4/foGbEsfidmVjqarD46rAXrMGTrfDkjsHiq2PhwnmsXr2KX//6Eg499IhuV2MNhe3bt/H55x/zxZxPicPL9P0qmbN9750mj8tzYbcavLxyKTfecDUnnnQqU6ee0C1O0AaV6JVSY4B9gXVa66WByY+GPKoYsO+++wE9ImGJdvSk7XrHju18+eVnzPnic1y1NXjTBuEaNBEjIQV7wWpsbvMiKceWRYCBp+9o6gYciC8lh4Jt3zNjxj/I7vsyvzr5VCZNmkxKSmp0VyiMXC4Xa9euYcWKZSz+6Tu2btuGzQqH93Vx1rBaMhL8vLXBSa3XwtwdCUzp72ZyfzcqzcOsjU7ee+8d3n33HYYPG85BEyYyZsz+jBgxkvj4+GivWjOWjo5gp5S6EbgQ+A44HHhLa/1wKILweHxGWVnzG/y88MJ/2bx5YyiKCFp9uUOGDIt42UOGDOPyy38T8XK7omGtPtaaurKzUxYDE1p6LxrbdSh5PB62bt3M8uW/8O13i9i4YR1YLHjSh1CXuz/+pKw98yau/Zy48r0XSXn75FE78vi9CzMM4ko3k7B7JdaqAmy2OMaOPZCJEw9j9Ogx5Ob2w2Lpfgc6LpeL/Pxd7Nq1g507d7Bt21Y2b1rPzl27MAwDqwVG9vEyPtvN4TluUuPNnPjl9gReXLv3xOulI6s4Nm9v81axy8qi/HgWFznYVGHDAGxWKwMGDGDwkOEMGjSYfv0G0L9/f3Jy+oV8B9DWdt1UMDX6C4AjtdZepZQdWASE5AfRms2bN7JSr8XnzAhnMS2y+O0ALHYadNIAAA0pSURBVNsW2fFAbDWdb/uL5o6xoWBPxHZVF3eMEd+ug1VXV8eOHdspKiqkqKiQ4uIiiooKyd+dz5bNm/bcHtCflIUnbwKerBEYdmfzBfm9bb+2WPBmDMWbMRRrTTH2ovUsWbmaJUt+BMCZlMywYcPJzsomMzOLrKwsMjOz6ds3h9zcfhE5mWsYBi5XLVVVVVRXV1NdXRV4bv5VVlZSVlZKSUkxJcWFlJSUUF3TeGeblQgDk+qYMNjLiD5eRvbxkNhCJvy5OL7Z64aJPtPh59QhLk4d4qLKY0GXxbGhws7Wqg2s+HEbXzXpuZqSnERGRiYZmdlkZGSSlpZOUlIyycnJJCUlNXhu/jkcjpDtWINJ9BattRdAa+1RSoX95pNlZaVEq/ODYU+MSrlgBNY7eEuXLiZ/53YSbJH/zuKt5gZpsxhs0ssiVq7bZwl8X51O9BHfrjtq5sxn+eijFq7ZslghIRmf3YkvcyS+5Gx8yTkY8e10kfTV4XA4OOWUU/jwww+p8tW1OqvfmYl7UCbugYdgrS3FVl1IXVUhyzbuxKbXY9S1PMTxHXfczfjxHapkBsXr9fLww/ezePGPbc5nsUBaAqTH+8iO9zEy3U96rp9cp4/cRB85Th8JHTz9UO62Nvq+yt2tj3uTbDc4KNvDQdl7N59ar4X8Giv5NTZ219ooq3NRWlNKaelmNtZZqXC3n92OPfZ4rrlmepcTfjCJ/mul1DvAV8BRwDddKlmI2BCz2/VXX81vNs3vSMWTNgRv+iD8SdmNetG0x+Kt45TTTmH69OkYhsFbH3zWgQ8F+tw7MyBb4fb7+P/27jU4yuoM4Ph/d7PJ5robA7mCARx5gpKQcBMcxnGkgoJo61Rn/CSO1vKlQ51addTppzptp9Zbp1CpWtva4lCVmQ4Uh1brFYECLXLLKQIScueSi7mRze72w25C7tnd7LKXPL+ZDPvuvrvvSXjeZ8+ec95nU1rPYWurxd5yBotn6PviiRPHopLo29pax03y2XYvpVl9zMjykJ/uIT/dS0G6hzyHl5QwP2h09Vm4664rf69Pdm4L6fnpKT5m53iYneMBwO2FC91WmrptNHfbaO62cq4jhZqOFDr7Rv9//OCD3axf/z0cDseojwcrlKJmj4vIWvxV/t4wxvx9UkcOgsuVS13T+WgfZlQWt/+7Na9+z96Cy5Ub1jMrKxfxdZjPnaxYz2mEKxZxHazXX3+Lzs5O6uvrqK+v5ezZMxw/cZwzp4/hbfwSX0YuvbmzcV8zG19azoRJ32dPZ8eOHfh8Pnbu3InPnh1cQ3xebB3NpFw6Q1rL1/jc3aQ5HJSVlyNzy5g581pKSmZQWFiM3W6PwG8+Ul7eNLZu3U57exutrS20trbS0nKJ1tYWLl68QHNzE+ebGzGN53G7r/S8rRb/UE2Bo5eCdC8FGZ6B3v00hxfbOG8COaneIX+vklTvhO3s88L5biuN3baBnnxTl5WmnlQu9sDgKdG0VDvTp+czd3YR+fn55OVNw+XKHfKTk5MTkRVQE07GishdxpgdIvLo8MeMMVsm3QJ0MnY4nYyNrNEmrWIZ15PV3d3Fnj2f8eG//sn/+guTOXLozSmhz1mCJ7sIbCMTrr35BI6zXwxs95Qux50/b9RjWHq7sLXXkdJWS2p7Pb6+y6TY7SxetITbbltFRUVlXC7B9Pl8tLRcoqmpkcbGhis/DXU0NNTTPeg7c21WKMzwMjPTzczMPkqzPczK7gt6Mrb1soUz36Rw9psUznXYqO1KpanLgndQSs3KzKCwsJjCohIKC4sGfgoKCnE6XZMakon0ZGxe4N/hRaCiPhAcy2TXP6GoyyuTVszierLS0zNYuXIVK1eu4sKF8xw8uJ9Dhw5y5Ohh3M0nwGqjL6uQPtdM+lzX4kvzJyv39DLsjceweHrpLVmIe7pceVGfD2vXRVJaa7C31WLt9C9CyHG6WLhiBVVVi6iqWkR6+iiTvHHEYrH4JzyvyWPevBuHPObz+Whvb6OhoZ6Ghnrq6mo5d66GU2dPs/f0xYH9pmfAPGcP83Pd5Ds89Hgs3Duni+X5l/msIZWjl1I50ZZGy6DvWS/Iz6f0xjksn3EtxcUlFBUVU1RUHDfLU4OpXvmHwE2PMean/feLiGZABYy8YOq++9bFXa9+uHDiWkSswCZgAXAZeMQY81VUGzqBadOms3r1WlavXovb7aa6+jiHDh3g3wf201SzF2r24svMo9dVijt3Nr7UDHxk4M4vA58PW3sDKS1fk9pWA5c7wWLh+uuFJYvXUFW1mNLSWQm5pHI0FosFp9OF0+mirOyGIY91dnZw5sxpTp/+CmNOcPDol3zS0OUf+nF46HBb2fhFHj194MzO5saFlcydW8acOdcxa9bsuH8DDKYe/cPAI8A8EVkTuNsKpAKjrqOLxxNCTWwq1boJJ66BbwMOY8xyEVkG/Aq4J+qNDZLdbqe8fAHl5Qt48MGHqaur5cCB/ezb/wUnT/6HtLpD+Kx2vGnZ2JurcTQdgZ5vsNtTqaxcyNKly6iqWozTOfXKFmdmZjF/fgXz51cA/i8ZOXbsCL9/Ywu1ded453QGS5bcxLp136Gs7IaEe/MLZujmLeAD4GngucB9XqB5nOfE9QmhFOHF9QrgfQBjzF4RifzykggqKZlBSckM7rnnXlpaLvHppx+xdeuf6Ou+hO3sHubKPO5YvYalS5eRlja5VR3JxmazUVFRyQsv/ob7778bgCeeeDbGrQpfKFfG2vEP/Nvx1wEpNsZsHWPfF4D9xpi3A9t1xpiSsV47WpNWk+mhTnYyNg6rMUbNaLVu4mnoZoIrY0OJ69eAd40xuwLbNcCc/nX4w12NK2NDtXv3Ln73u00AbNv2t4TrmcbCxx9/SGZmFosXL411U4aI1pWx7+E/GUoAG1APjHpCADlA26Btj4ikjHVCxCOX6+pfjatiIpS4bgcGr0m0JlJMA6xadSfvvbeNysqFmuSDlAwdtlAS/bTAUMxrwA+A8brKcXFChFuNUYVm48Yf8/LLvxzYfuyxJ2PYmpCFEtefA+uAbYEhySNXo4GR9tJLm6O23l3Fp1CuGev/DJppjOlm/GVonwNrABL5hFDBWbHiliHbN9+8IkYtCUsocb0d6BGRPcCLwGPRblw0OByOuFwDr6In1OqVLqAX/2RrhzHmW2Ps27/qpgL/uOdDxpjqcV7+PHA2hHarONPW1pbb2Ng4p7Cw8JTT6WyNdXuGKQWmj/ZAKHEdBo1rFU1jxvVwoQzdfNcYcwuAiOwETo61ozHGC2wI4bWDaqyKX06nM1GX5QUd12HQuFZxIZRE7xOR7YDBvwwN/EvTlEpkGtcq6YWS6N+IWiuUih2Na5X0gh6jV0oplZii/5UwSimlYkoTvVJKJTlN9EopleRCmYydErTyZnhE5CbgF8aYW2PdFjU6je3wJENsa49+pIHKm8BT+CtvqnGIyBPAa4CWQIxvGtshSpbY1kQ/0pBStARZHW6KOwXcG+tGqAlpbIcuKWJbE/1Io1bejFVjEoEx5l3AHet2qAlpbIcoWWJbE/1IcVF5U6ko0NieojTRj6SVN1Wy0tieovRj20jbgdsDpWgtwEMxbo9SkaKxPUVpCQSllEpyOnSjlFJJThO9UkolOU30SimV5DTRK6VUktNEr5RSSU6XV0aQiNwBXGuM2RLGcx1AtTFmVojPexN4O7AZ0rFF5BrgDmPMX0I5pppaNK4Tnyb6CDLGvJ9gx64A7gb0hFBj0rhOfJroI0hE1gNlwA2AE8gAnjHG7B5j/yzgz0Au8NWg+z8CNhhjqkVkA1AIvAn8FWgAZgC7jDHPDD+2MeYpEXkWf6XCFGCzMeZVEfkZ/iJWecBhY8xDwDPAAhF5FNgFbAHSgW7gUWPMuQj8WVSC07hOfDpGH3nXAdOAdcADjP9mugE4aoy5BXg1iNeeBawHlgC3icjC4TuISBVwJ3ATsBSYKyJOoMUYczv+k2KZiJQAzwEfBj4WPw+8Eqi5/Tzw8yDao6YOjesEpj36yDsF7AC2AnbglXH2nQvsBDDG7BOR0arkWQbdPmyMuQQgIvsAGWV/AfYbYzyAB/iRiNiBfBHZCnQAWYG2DVYOPC0iTwaOmfAV+1REaVwnMO3RR971QLYxZi3wIPDrcfY9DiyHgR5Lf5D2AEWB24N7N/NEJENEbPh7NsdHec1qYKGIWEXELiL/wF/IaqYx5gHgafwfYy2AlysxUA08Gej5fB//x2ml+mlcJzDt0UfeSeBWEbkff7D9ZJx9fwv8UUQ+wx+QlwP3vwJsEpEaoG7Q/r34A7UAeMcYc1hkaOfHGPNfEXkff6VCK7AZ2Ac8KyKfAD7gNFCMv5dWLiI/BB4HNgdWSaQDG8P8/VVy0rhOYFrULEGIyCzgbWPMsli3RalI0bi+OrRHfxWIyCb8KxaGu9MY032126NUJGhcJw7t0SulVJLTyVillEpymuiVUirJaaJXSqkkp4leKaWSnCZ6pZRKcv8H2yKbVzmJjZoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['ratio_len1_len2'] = df['ratio_len1_len2'].apply(lambda x: x if x < replace_value else replace_value)\n",
    "plot_real_feature('ratio_len1_len2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['log_ratio_len1_len2'] = np.log(df['ratio_len1_len2'] + 1)\n",
    "plot_real_feature('log_ratio_len1_len2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "sns.pairplot(df.loc[ix_train][df.columns[7:].tolist() + ['is_duplicate']], hue=\"is_duplicate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.jointplot('log_ratio_len1_len2', 'abs_diff_len1_len2', df.loc[ix_train], alpha=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_corr(predictors):\n",
    "    predictors = predictors[:]\n",
    "    predictors += ['is_duplicate']\n",
    "    mcorr = df.loc[ix_train][predictors].corr()\n",
    "    mask = np.zeros_like(mcorr, dtype=np.bool)\n",
    "    mask[np.triu_indices_from(mask)] = True\n",
    "    cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "    g = sns.heatmap(mcorr, mask=mask, cmap=cmap, square=True, annot=True, fmt='0.2f')\n",
    "    g.set_xticklabels(predictors, rotation=90)\n",
    "    g.set_yticklabels(reversed(predictors))\n",
    "    plt.show()\n",
    "    \n",
    "plot_corr(df.columns[7:].tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Проверяем новые признаки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictors = df.columns[7:].tolist()\n",
    "print (predictors)\n",
    "\n",
    "def check_model(predictors):\n",
    "    classifier = lambda: SGDClassifier(\n",
    "        loss='log', \n",
    "        penalty='elasticnet', \n",
    "        fit_intercept=True, \n",
    "        n_iter=100, \n",
    "        shuffle=True, \n",
    "        n_jobs=-1,\n",
    "        class_weight=None)\n",
    "\n",
    "    model = Pipeline(steps=[\n",
    "        ('ss', StandardScaler()),\n",
    "        ('en', classifier())\n",
    "    ])\n",
    "\n",
    "    parameters = {\n",
    "        'en__alpha': [0.00001, 0.0001, 0.001, 0.01, 0.02, 0.1, 0.5, 0.9, 1],\n",
    "        'en__l1_ratio': [0, 0.0001, 0.001, 0.01, 0.1, 0.3, 0.5, 0.75, 0.9, 1]\n",
    "    }\n",
    "\n",
    "    folder = StratifiedKFold(n_splits=5, shuffle=True)\n",
    "    \n",
    "    grid_search = GridSearchCV(\n",
    "        model, \n",
    "        parameters, \n",
    "        cv=folder, \n",
    "        n_jobs=-1, \n",
    "        verbose=1)\n",
    "    grid_search = grid_search.fit(df.loc[ix_train][predictors], \n",
    "                                  df.loc[ix_train]['is_duplicate'])\n",
    "    \n",
    "    return grid_search\n",
    "\n",
    "if not os.path.isfile('./../data/tmp/1_model.pkl'):\n",
    "    model = check_model(predictors)\n",
    "    print (model.best_score_)\n",
    "    print (model.best_params_)\n",
    "    with open('./../data/tmp/1_model.pkl', 'wb') as f:\n",
    "        pickle.dump(model, f)\n",
    "else:\n",
    "    with open('./../data/tmp/1_model.pkl', 'rb') as f:\n",
    "        model = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "Fitting 5 folds for each of 90 candidates, totalling 450 fits\n",
    "[Parallel(n_jobs=-1)]: Done  18 tasks      | elapsed:   21.6s\n",
    "[Parallel(n_jobs=-1)]: Done 168 tasks      | elapsed:  2.1min\n",
    "[Parallel(n_jobs=-1)]: Done 418 tasks      | elapsed:  5.2min\n",
    "[Parallel(n_jobs=-1)]: Done 450 out of 450 | elapsed:  5.5min finished\n",
    "0.633560068268\n",
    "{'en__l1_ratio': 0, 'en__alpha': 0.02}\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"./../images/htop.png\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred = model.predict_proba(df.loc[ix_test][predictors])[:, 1]\n",
    "y_test_pred_fixed = link_function(y_test_pred)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = plt.subplot2grid((1, 2), (0, 0))\n",
    "ax2 = plt.subplot2grid((1, 2), (0, 1))\n",
    "sns.distplot(y_test_pred, ax=ax1)\n",
    "ax1.set_title('Model prediction')\n",
    "sns.distplot(y_test_pred_fixed, ax=ax2)\n",
    "ax2.set_title('Model prediction fixed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.isfile('./../data/tmp/1_pred.csv'):\n",
    "    pd.DataFrame.from_records(\n",
    "        zip(df.loc[ix_test]['test_id'].values, \n",
    "            y_test_pred), \n",
    "        columns=['test_id', 'is_duplicate']).to_csv('./../data/tmp/1_pred.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.isfile('./../data/tmp/1_pred_fixed.csv'):\n",
    "    pd.DataFrame.from_records(\n",
    "        zip(df.loc[ix_test]['test_id'].values, \n",
    "            y_test_pred_fixed), \n",
    "        columns=['test_id', 'is_duplicate']).to_csv('./../data/tmp/1_pred_fixed.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "- без поправки: **0.52929**\n",
    "- с поправкой: **0.44127**\n",
    "- предыдущее значение: **0.46258**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "display(\n",
    "    eli5.sklearn.explain_linear_classifier_weights(\n",
    "        model.best_estimator_.steps[1][1],\n",
    "        feature_names=predictors))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Буквенные n-граммы\n",
    "<br />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 180 ms, sys: 24 ms, total: 204 ms\n",
      "Wall time: 267 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "if os.path.isfile('./../data/tmp/cv_char.pkl') and os.path.isfile('./../data/tmp/ch_freq.pkl'):\n",
    "    with open('./../data/tmp/cv_char.pkl', 'rb') as f:\n",
    "        cv_char = pickle.load(f)\n",
    "    with open('./../data/tmp/ch_freq.pkl', 'rb') as f:\n",
    "        ch_freq = pickle.load(f)\n",
    "else:\n",
    "    cv_char = CountVectorizer(ngram_range=(1, 3), analyzer='char')\n",
    "    ch_freq = np.array(cv_char.fit_transform(df['question1'].tolist() + df['question2'].tolist()).sum(axis=0))[0, :]\n",
    "    with open('./../data/tmp/cv_char.pkl', 'wb') as f:\n",
    "        pickle.dump(cv_char, f)\n",
    "    with open('./../data/tmp/ch_freq.pkl', 'wb') as f:\n",
    "        pickle.dump(ch_freq, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unigrams: 1779\n",
      "Bigrams:  10723\n",
      "Trigrams: 74809\n"
     ]
    }
   ],
   "source": [
    "unigrams = dict([(k, v) for (k, v) in cv_char.vocabulary_.items() if len(k) == 1])\n",
    "ix_unigrams = np.sort(list(unigrams.values()))\n",
    "print ('Unigrams:', len(unigrams))\n",
    "bigrams = dict([(k, v) for (k, v) in cv_char.vocabulary_.items() if len(k) == 2])\n",
    "ix_bigrams = np.sort(list(bigrams.values()))\n",
    "print ('Bigrams: ', len(bigrams))\n",
    "trigrams = dict([(k, v) for (k, v) in cv_char.vocabulary_.items() if len(k) == 3])\n",
    "ix_trigrams = np.sort(list(trigrams.values()))\n",
    "print ('Trigrams:', len(trigrams))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 6.83 s, sys: 9.49 s, total: 16.3 s\n",
      "Wall time: 21 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "def save_sparse_csr(fname, sm):\n",
    "    np.savez(fname, \n",
    "             data=sm.data, \n",
    "             indices=sm.indices,\n",
    "             indptr=sm.indptr, \n",
    "             shape=sm.shape)\n",
    "\n",
    "def load_sparse_csr(fname):\n",
    "    loader = np.load(fname)\n",
    "    return sparse.csr_matrix((\n",
    "        loader['data'], \n",
    "        loader['indices'], \n",
    "        loader['indptr']),\n",
    "        shape=loader['shape'])\n",
    "\n",
    "if os.path.isfile('./../data/tmp/m_q1.npz') and os.path.isfile('./../data/tmp/m_q2.npz'):\n",
    "    m_q1 = load_sparse_csr('./../data/tmp/m_q1.npz')\n",
    "    m_q2 = load_sparse_csr('./../data/tmp/m_q2.npz')\n",
    "else:\n",
    "    m_q1 = cv_char.transform(df['question1'].values)\n",
    "    m_q2 = cv_char.transform(df['question2'].values)\n",
    "    save_sparse_csr('./../data/tmp/m_q1.npz', m_q1)\n",
    "    save_sparse_csr('./../data/tmp/m_q2.npz', m_q2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Jaccard similarity и производные\n",
    "$$\\large\\begin{array}{rcl}\n",
    "J\\left(A, B\\right) &=& \\dfrac{\\left|A \\cap B\\right|}{\\left|A \\cup B\\right|}\n",
    "\\end{array}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Униграммы "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "v_num = (m_q1[:, ix_unigrams] > 0).minimum((m_q2[:, ix_unigrams] > 0)).sum(axis=1)\n",
    "v_den = (m_q1[:, ix_unigrams] > 0).maximum((m_q2[:, ix_unigrams] > 0)).sum(axis=1)\n",
    "v_score = np.array(v_num.flatten()).astype(np.float32)[0, :]/np.array(v_den.flatten())[0, :]\n",
    "\n",
    "df['unigram_jaccard'] = v_score\n",
    "plot_real_feature('unigram_jaccard')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# учитываем каждую букву больше одного раза\n",
    "v_num = m_q1[:, ix_unigrams].minimum(m_q2[:, ix_unigrams]).sum(axis=1)\n",
    "v_den = m_q1[:, ix_unigrams].sum(axis=1) + m_q2[:, ix_unigrams].sum(axis=1)\n",
    "v_score = np.array(v_num.flatten()).astype(np.float32)[0, :]/np.array(v_den.flatten())[0, :]\n",
    "\n",
    "df['unigram_all_jaccard'] = v_score\n",
    "plot_real_feature('unigram_all_jaccard')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# учитываем каждую букву больше одного раза\n",
    "# нормируем максимальным значением, а не суммой\n",
    "v_num = m_q1[:, ix_unigrams].minimum(m_q2[:, ix_unigrams]).sum(axis=1)\n",
    "v_den = m_q1[:, ix_unigrams].maximum(m_q2[:, ix_unigrams]).sum(axis=1)\n",
    "v_score = np.array(v_num.flatten()).astype(np.float32)[0, :]/np.array(v_den.flatten())[0, :]\n",
    "\n",
    "df['unigram_all_jaccard_max'] = v_score\n",
    "plot_real_feature('unigram_all_jaccard_max')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Биграммы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "v_num = (m_q1[:, ix_bigrams] > 0).minimum((m_q2[:, ix_bigrams] > 0)).sum(axis=1)\n",
    "v_den = (m_q1[:, ix_bigrams] > 0).maximum((m_q2[:, ix_bigrams] > 0)).sum(axis=1)\n",
    "v_score = np.array(v_num.flatten()).astype(np.float32)[0, :]/np.array(v_den.flatten())[0, :]\n",
    "\n",
    "df['bigram_jaccard'] = v_score\n",
    "plot_real_feature('bigram_jaccard')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/Cellar/python3/3.6.3/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/ipykernel_launcher.py:6: FutureWarning: using a dict on a Series for aggregation\n",
      "is deprecated and will be removed in a future version\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>p1</th>\n",
       "      <th>p99</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_duplicate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.083333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.116213</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   min  max        p1  p99\n",
       "is_duplicate                              \n",
       "0             0.000000  1.0  0.000000  1.0\n",
       "1             0.083333  1.0  0.116213  1.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[ix_train].groupby(['is_duplicate'])['bigram_jaccard'].agg(\n",
    "    {\n",
    "        'min': np.min,\n",
    "        'max': np.max,\n",
    "        'p1': lambda x: np.percentile(x, q=0.01),\n",
    "        'p99': lambda x: np.percentile(x, q=99.99)\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество аутлаеров справа: 0\n",
      "Количество аутлаеров слева:  0\n"
     ]
    }
   ],
   "source": [
    "print ('Количество аутлаеров справа:', (df['bigram_jaccard'] > 1).sum())\n",
    "print ('Количество аутлаеров слева: ', (df['bigram_jaccard'] < -1.47).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[df['bigram_jaccard'] < -1.478751, 'bigram_jaccard'] = -1.478751\n",
    "df.loc[df['bigram_jaccard'] > 1.0, 'bigram_jaccard'] = 1.0\n",
    "plot_real_feature('bigram_jaccard')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UikNESrlT2DvN/QQ7tPEMbCfxW9irVra1YGAqyKD1xfBaY34nlt9ge28P1diO9l/JJiuKKroO5SNQKBSKfk6vGBGYpmklEkphKRQKxaHgdBpl2KG+bdIrFEEiYVFZ2Z5oPIVCoVA0kJsbbNcsfxU1pFAoFP2cXjEiUCh6KhEL6mLNJxH7nAbu9qxAo1D0AFKqCIQQedgzRudJKdelUhaF4nCoiyV4f13zKQNzxubhdnXbkv0KRYdImWkouYTvwxzaOvwKhUKh6GRS6SO4C3tj59Y28lAoFApFN5ASRSCEuBwobdgCTqFQKBSpI1Ujgm8B84QQ87H3pf1X443fFYreimlZxE2TSDyBmqyp6C2kfGZxUhl8uy1ncSyWsNQ8AkVPwbQsNpaGWLmnmtXFtSzfUUVNJE4oGifWaOKjBvhcBl6ngc9lEHQ7yA24yA+6GZblY1SOn1E5foIeFbyn6Bpyc4Of0o4d0NQbqFC0g+pwjI83V/DBxnKW7aikOhwH7IY+y+dkYLqHgMvA7dDRdY1ReUHCsTh1sQThqEldLEFNOM7WvfUs3V5JKLo/5DQ/6GZ0rh+RF2BcfpBx+QFyAy40TcWfKrqHlI8I2oMaEShSQcK0WLxtLy+uLOLDTeXETYscv4uZhZlMG5rBpEFpeD1O5svSZtfOHJ3Lwg3Nz584Lh/TNCmtjbK1PMTmsjq2761jQ0mIHXvrMJM/xyyfk7H5QUSenzG5AUbn+hmS6SWGpuYtKNqNGhEoFIdJPGHy+toSHl2yg+1768nwOjl/ykDmiVzGDwiiN+qp7422Z0fK/dTHzQMURH7AxVlTBrFwQynRhElJTYQ91RGKqsPsrg6zeGsFDdYmr1NneLYft0O3zUuZXjJ9TjRNU/MWFB1CKQKFIollWby/oYz7PtzC7qowo3L93HLyGGaNzMZp6PicBnoX9rpdhs7gDC+DM+y94eeMzcOna2wpD7G+NMT6klrWFNeypqiGz3dWAZDucTA824dh6JwwIosMb6r2ulf0ZpQiUCiAzeUhfv/uRj7dUcXwbB/nTi5gVI4fy7T4eEMZkJrZwm6Hztj8IGPzg4A9AnlvbTEVdTG2VtSxtbyOtcW1LN8luQMYmx9gxrBMZgzNYOzAdBItmH6VGUnRFKUIFP2aUNzkH4u288TSHXhdBt+dPYIvjx/Aghbs+92NpmnNTE8Jyz6f7XeR7Xdx1JAMTNOiINPLmt3VLN22l8eX7eSxJTtwGToD0twMzvAwMN3DoHQvPpehzEiKZihFoOi3rC2u4fbXJZvL6xiXH+DLY3PxOQ3MVtK31jB3FU39CWA7oZui6xoTBqYzviCNc48aTF00zspd1SzbUcnizRUs3LqXhoFBps/J0h2VTBuczuRB6YzI9qnoJIVSBIr+RzRu8teF23h86Q7SfU7OnVTA6LzAQa9rb8OcClqS7YpZwxmfFyCaMCmqCrMr+bd0WyXvrLPTDkz3cMLIbI4fmc3kwek4utIJouixKEWg6FdsLg/x01fXsaE0xBkT87n8S4Us3VKRarG6FJehMzTLx9AsHwCzRS519TGWbK/kw43lPLtiN//9bBdBt4Ojh2dywqgcpg7N2OcgV/6Evo9SBIp+Qdi0ePKzXfz14614XQa/On0cRw/P6lLTTk9F13X8PhdzxuYxZ2we9dEES7dX8sLyXXy0sZx31pXiceiMyQtwwfTBzBmRrUYKfRylCBR9jqabxVTWx7j7nQ0s3rqXEdk+TpuQT10kzvvrSnqMaac7ac3ElYgniJsmW5KRSOuKa7nlxTVkeJ2cODqHuSKHqYMzMJRS6HMoRaDoczTeLGZzeYhXVhUTiZvMFTlMG5KhnKNt4NB1RucGGJ0bIJ4w8XmcLNxUzutri3nuiz1k+ZycNCaXeSKXSYPSDphcp+i9KEWg6JPETZP5G8pZur2SHL+L35w1kaK9apmSQ8Fh6Mwamc3p4/IIxxJ8vLmCd9aX8tKqIp5evptsv4sTRucwd2wuI3P8+F0O5U/opShFoOhzbKuo47HFOyipjTJ1SDonjs5heI5fKYLDoHHI7FHDszhqeBbfjSb4ZEsFz32+ixdW7Oa55bvJDbg488gCvjZxAHlBd4qlVhwqShEo+gyWZfHMij38cf5mDF3j3MkFjM49eFioonVa8icAzBqdi25Z1EUTrCuuYdWeGh75ZBt//2Qb04dmcOr4fOaMzsGnJq71CpQiUPQJ9tZF+dWb6/locwXThmYwszCTgFu93l2Nz2UwdUgGU4dkMGZAkAUbynhtbQm3vyH57TsbmDM6h1PH5zF9aKZyMvdg1C9F0etZuLWCX7yxnupwjO/PHsHJEwfwQQtLQyu6lsGZPs6fPoTzpg1m9Z4a3llXwgcbynh9bQm5ARcnj83jqxPzGZHtT7WoiiYoRaDotYRjCe7/aAtPfr6b4dk+/nT2RMbkBQ55aWhF59DUjDRpYBoT8gN43E7eXlfMfz/bxePLdjJ5cDrnTRnEiaOaz09oGvoLapG87kApAkWvRJbUctur69hSUccFUwfxnWML8TiVPbqn4TB0pg/PIh5PMGNoBl/squaznVXc+vIaBgTdnDOpgLOOKCDDZy+f3Tj0twG1SF7XoxSBoleRMC2e+HQnD3y8lQyvkz+fM5FjCrNSLZaiHfhdDmYOz+LoYZl43A5eW1XEXz7eyt8WbmPe2DzOnVTAwOQyGIruRSkCRa+hqDrM7W9IPt1RxZzROdwyb7TaiKUXouvavvkJm8tDPP35bl5dU8yrq4sZmO6hMMvHhAFBcgKuVIvab1CKQNHjsSyLF9eU8Mf3NmJaFj+YO4p5Y/NwOh0t+gP64/pBvZUR2X5+NHc03zluOO+tL+OVtcUs3FLBJ1sqyAu4GDcgyMi8AJkDgqkWtU+jFIGiR1NUHebOdzbwyZa9DM7wcPqEATg1jfmytNUN4vvj+kG9nYDbwRlHDOA4kcvLK3azNrkl5wcby/lgYznDMr3MHp3D7FHZzfaNVnSclCgCIYQT+AdQCLiBX0spX0qFLIqeiWVZPL+yiD99sJmEaXHd8cNJcxlqnaB+QMDtYPrQDKYPzaA6HEPTdZZsqdi381puwMUJI7M5dmQ2kwel4Xep/mxHSdUd/AZQLqW8RAiRBSwHlCJQAPZCcX94bxPLtlcybWgGt84bjd/nahZNoui9tLTbGzQ366V5nJw4Lp8zjiygJhxn8dYKPtlcwcuri3lmxR50DURegLH5AcbkBhie7WNQuofcgFtNYDsEUqUIngaeSf6vAfEUyaHoQVTURXnwk2289MUevE6DG+eM5NQJ+Wiapuz+fYzWlq5oyazXOK0BHDcii2OGZZCb5uWLXZWs2lPDO7KM578o2neNoUG610m614nHoaNrGoae/NNA1zR0XcPvdpDpc1KQ7mHa0EwK0j39ct5CShSBlLIWQAgRxFYIP02FHIqeQSRu8r/PdvHPxdupjyWYMjidY0dk43PozE/OEFZ2f0VjnIbOuIFpVIYiDM3wYo21qA7HGZYTYE9VPcU1EarqY1SH40TjJhYQT5iYlkUsAaZlkrAstu+tp6QmQty0exo5fhffmDGECyYP7FcjipQZ14QQQ4DngQeklE+kSg5F6ojGTV5eXcSji3dQVBPhuBFZXDZzGJtKalMtmqKXoWka6V7nPuVQmOmFTO++79sKLPhkfQl762NsKguxpqiWP76/iZdXFnH9CSOYMDBtX9q+PFJIlbM4H3gLuF5K+W4qZFCkjnAswTNfFPGfpTsoC0UZNyDI908axZQhGSQslCJQdCuappHlc5E11MW0IRkkNJ2HP9rMTc+t5NTx+RyRVAZ9eYZzqkYEtwCZwG1CiNuS574ipaxPkTyKbqAumuDZFbt5fNlOKupiDM30cqEYxLAsL5WhaL/dOlLRc9A0jeNG55CIx3luxR5eWV1MTSTOzMLMVIvWpaTKR3AjcGMqylZ0P5X1MZ7/Yg//WbaTqnCco4dlcN5RgymviaRaNIWiRdwOg/OnDOLV1cV8sLEcXYMTx+WnWqwuQwXgKroMWVzLU8t38cbaUqIJk6MLM7lo+hDGDQiSsOBDqcJBFT0XQ9f46sR8TMvi/Q3lHDeqnK+Oz0u1WF2CUgSKTiUcSzB/YznPLN/Nit3VeBw688blUhB0kxtwU1RZT1FlvTIBKXoFmqZx2oR8qsJxfvf2ekZmeRnfB5e7UIpA0WFMy+LTHZW8vqaE9zaUEYomGJTu4fuzR3D6hHwSuq4mgyl6LU5D59xJBTz5+W5+/PIa/nPJUQQ9favp7Fu1UXQr2/fW8/KqIl5bU0xJbRSfy+DYkdmcJHKZNDgdXdNIoBaBU/R+/G4Ht5wi+L9nV/Kbtzfwm9PH9qnlTpQiUBwS9bEE78hSXlxVxIpd1egaTBuayRWzCplRmMWSzeVUhqIHbBWpzECKvsD4gjQuO3oo/1i4jSNXpHPK+Pw+M7dAKQLFQbEsi1V7anhxVRFvryulLpZgUIaH2aOymViQZg+TTQvD0FMtqkLRZdTHTfL8ToZlefnz+5sI1cc456jBfWJugVIEilapqIvy0uoSXl65h+1763E7dE4YncMp4/MZOyDIR+vVBvGK/oWmaXx1wgAeWbiNV1YXc9aUQakWqVNQikBxAHHTYuGWCl5aVcRHmytImBaD0j18ZVwe4wYEcTt0SqvDjMrve5ETCkV7CHoczBuby8urinnm8118e+awVIvUYZQiUACwtbyOl1cX8dqaEspCUbJ8Ti6cOogTRC6b1ZIPCsUBTBgQZH1JiH8t2s7cUTmMyvWnWqQOoRRBP6Y2EuctWcorq4pYuacGQ4PphZlcN24ExxRm4jB0EhZKESgUTdA0jVPG5fLYkgg/e30dj108BWcv9pEpRdDPMC2LhdsreWllEQs2lROJmwzL8nLVrEJmj8ll1c5KYrEEH20oA1TEj0LRGj6Xg++fOJKfv7qORxZt59pZhakW6bBRiqCfsKuqnldXF/PK6mL2VEdwO3TGDwhy5MA0CtLcaJpG0OtMtZgKRa9i5ohsTp+Qz2OLt3P8iCwmFKQd/KIeiFIEfZhwLMF7G8p4eVURy3ZUoQEzhmVw+cxhxGKJXj2UVSh6CjfNGcnS7ZX87HXJYxdPIeDufc1q75NY0SamZfH5zipeX1vCO7KUUDRBQZqHy44ZyryxeeQF3WrBN4Wik9A0jRhw87zR/PD5Vfz8Dcmtpwj8LkevmmimFEEfwLIs1peEeGNdCW+tK6GkNorXqXPimFzmiFzKqsNomsbqXVWsRtn9FYrOovF+yieMyub9DeW43t7AzV8e06smmilF0IvZsbeeN9eV8Oa6ErZW1GPoGtOHZXDFrEKOGZ6F12nYvX+17r9C0eUcPSyTHXvDvLe+lHnj8jhxZHaqRWo3ShH0IkzLYl1xLR9sKufDjeVsLAuhAVMGp3PmpIFgWvhcBpgWizaVA6r3r1B0F5qmcfrEfP6zbCc/f3UteecdycRe4jxWiqCHU1kX49OdlSzZVslHm8sprY2iazChII2rjy3k+FE5yu6vUPQQvE6DC6YO4pnle7jxuVU8dP6RjM4NpFqsg6IUQQ+jLBRl9Z4aPttZyZLtlWwsDQHgdeocNTSTS4/JIh5L2D1/UHZ/haKHEXA7+O1ZE/jBcyu58r8ruP0rgjmjc1ItVpsoRZBCaiNx1hXXsrqohjVFNawuqqE4ac93GRrjC9I4fmQ2w7K8FKR5MHSNmaNz9zmnFApFz6Qg3cM/L5rCj15aw80vreHS6YO5auYwPM6e6UBWiqAbSJgWu6rCbCoL7fvbWBZiW0U9DXu2DEz3MG5AkLMmFSDyg4zO8+MwDGXuUSh6IZqm4XI7+N3ZE3ngg838a+lOXl9bwrdnFXLquDwcPWwOT0oUgRBCBx4AJgER4Eop5cZUyNIRonGTqnCMqvo4lfWx5P8xSmuj7KmJUFwdZk91hOKaCHHTbvI17N7CsCwfx4/OZWx+gJG5AZZv37sv39LqMKXVYWXuUSh6KY3DSicNTCPT62TZjkp+9eZ67v9wC3NFLrNHZTN+QLBHTEBLlQRnAR4p5UwhxDHA3cCZnV1ITTjOa2uKicRNEpZFwrQwk8eExQGf46ZFNG4STZjEElbyaBKm/HXqAAAgAElEQVRNWPYxfuD52kiCuliixXI1IDvgIi/oZnRegGNHZjM400thto/BmT6WbC7fL2N9DG8PeBEUCkXXMTTTy6XHDGX1zipeW1PMS6uKeHr5bjRgSKaXgjQ3OQE3AZeBy9BxOXRchk7AbXDahHz8rq5tIzTL6v4NZYUQ9wBLpJT/S37eJaVsa4eHUmBbtwinUCgUfYdhwEFNC6nqiqYBVY0+J4QQDillvJX0ykaiUCgUXUSqPBbVQOMtrvQ2lIBCoVAoupBUKYIFwKkASR/ByhTJoVAoFP2eVJmGngfmCSE+wfatfjNFcigUCkW/JyXOYoVCoVD0HHrWrAaFQqFQdDtKESgUCkU/p1/PZDrYDGchxFXANUAc+LWU8pWUCHqYtGcGtxAiF9t5f6SUMtz9Uh4e7Xh23wcuSH58TUr5i+6X8vBpR/2+A1wOWMBdUsqnUiHn4dDO91IHXgVelFI+1P1SHj7teHb3AccCNclTZ0opq5pl1I309xHBvhnOwI+xZzgDIIQYAHwXmAWcDNwphHCnRMrDp9X6AQghTgbeAgakQLaO0tazGwFcDHwJOAb4shDiyJRIefi0Vb8c4Frs+p0E3C2E6EUbI7b9Xib5NZDZrVJ1Hger31HAyVLK2cm/lCoBUIrgWOANACnlImBao+9mAAuklJHkg9oI9LbGpK36AZjAXKCim+XqDNqq2w7gFCllQkppAU6g14x2krRaPyllGTBZShnDVuLhZD17C22+l0KIc7HfzTe6X7ROodX6JUcLo4G/CiEWCCG+lRoRD6S/K4IWZzi38l0NkN5dgnUSbdUPKeXbUsry5pf1Clqtm5QyJqUsE0JoQoi7gM+llOtTIuXhc7BnFxdCXA8sAh7vbuE6SKt1E0JMBC4CfpYKwTqJtp6dH/gz8A3gFOC6njBa7e+KoK0Zzk2/CwKV3SVYJ9GXZ3C3WTchhAf4TzLNdd0sW2dw0GcnpbwfKACOF0LM6U7hOkhbdbsUGAS8h+0D+T8hxCndK16Haat+dcB9Uso6KWUNdj0ndbeATekV8whM07QSiZ4vp0KhUPQknE6jjB686NwhkUhYVFbWpVoMhUKh6FXk5gbbtWpzr1AECoWidSxr//4aDX+GruF26Bh6bwomUqQKpQgUil5ERV2UBZsr+KKohvXFtZTURqisi+3bAa8pjqRCyPQ5GZzhZUyun6lDMpg6OB1vD90/V9H99AofQSyWsJRpSNFfiSdM3ttQxjMr9rB8ZxUW4HMZZPtcZPgc+F0OnIZGYU6AXRV1aBqYFgzJ8hGJJ4jGTSpCUXZVhdlaXkfctHA7dL40Ios5Y3I5bmQ2AUd/jxvpm+TmBj+ledh4M9SIQKHoocQSJi+sLOKfi7dTWhtlYLqHi6cPYdbILIZm+/l4fekB6WeOzt23T25LnwGmFGbx3Kc7kCUhFm2p4P31ZQQ9Do4flc2cMblMHJhGwOXArSxK/QqlCBSKDuAhhB6rbXbedAYI4z+sPC3L4v2N5dz3wWZ2V4WZWJDG7FE5jMzxoWka28vrGJR1eHl7nAbDs/0Mz/YzT+SypbyOklCUt9aU8OqqYtI8Dk4Zn8+ZE/IZnetH05RG6A8oRaBQdAA9Vou5/u3m58fMA+ehN9Y7K+v57TsbWLytkpE5Pu47eyJjB6YxX5Ye/OJDxNA1RuX6ueRLhUwbUsyGkhCri6p5dvlunvpsF8OzfZwyNo8vj81lcIa308tX9ByUIlAoWiMRRUtEsRxe0LvWsWpaFk9/vpv7P9qCrmtcd/xwvnpEAYau0R1TaFyGzoSCIBMKgkwdlsmnWyt4c20JDy7YyoMLtnJEQRqnjMtlrsgly+fqeoEU3YpSBAoFYOzdhHPHhzhLVuAoXYkeKkKP7F8lwHJ4SKQPJ541hnj+FKJD55DIGNEpZe+srOeXb67n851VTBuawdHDMkhzO/gw6QOYOfqg84FsGRMxBnujZLhMDM0isXcXkzISB6RpzzlnXZRZ+TArP5OEmUF9zKQ+liCeCLFtZ4hih47PZeAydJTlqHvRdQO324vfn4bD4ey0fJUiUPRb9JrdBNY8hnPjq+iVWwEwvdlY+RNIFEwm7s3EMtxoiQhapAatehfOok/xbHgRuJ14eiHmmNOIOjKw3ME2y2qNl1cV8ft3N+IwNG47eQzHjs45LDOQSzexassYlh3A4fSi6QYBr4tQOHZAOr/HedBzQU/zBkbTNMKxBNXhGNXhOCHTImbopHsdpHmdGEmNoGsaaupC12BZFolEgnA4REVFMVlZ+Z2mDFKqCIQQecCnwDwp5bpUyqLoPziKPsP32V9wbX0bLItExgiio84gnj0Wy5OJe9QsIps+OfCiAOgz/486Zz569Q5c2+fj3vgSrqV/wanpxPOnEBl2EpY3q10yROImf3hvIy+uLGLS4HRunjea3ID7sM1Aee44fn86luE7vAwaYUKLyiIaT+Bx6LgDLsIxk0jcpLQ2Slkoitdp4HcZZPpc6GqY0CVomobD4SAQsNe+DIWqSU/P7pS8U6YIhBBO4GGgPlUyKPomLUXymM4A8dLN+Bb9Hvf29zHdGdRPuRbGnEyiaM0h5W+mDSE88RLCEy/BX7oYx4K7ce5egqP4c2IFM4gM/3Kb11fVx/jBi6tZvquaC6cNZmiGh1U7bTNUe81ATclwmbg9fsKxrncoaIDXqZMTdFNZF6UumqA+lqAumiAUTZDpdRL0OJRC6EI8Hj8VFUWdll8qRwR3AQ8BP0mhDIo+SNNIHi1ai2/vavS1L4A7jeiMG4hNOB+cPhzEDlkRNMbKKCQy+gyiQ0/Ate19nLsX4yj5gphhwsSrQds/UStiwebyELe8uIbi6jC3nDyG40bn8qEs6VB9AQzNQtcN7M30ug+nrpHucRB0O6iLJQjHEuyqCmPUaKR5HKR7nbiMAyerKfNRxzEMA9NMHDxhO0mJIhBCXA6USinfFEIoRaDoGswEzt2LcG99GxIxYoOPJVJ4Ejg8sGUBANrIL3VKUZY7nciYs4gNPBr3hhdxf/hr0je9T82Jd2GmDQFgQ2ktNz79BdG4ydenDsIyLWKtLA1xOKQy5l/XIOAyKEjzEI4lqKqPUVUfY29dDKeh4XUaeJ0Gumb7INRooWN09rNO1YjgW4AlhJgLTAb+JYQ4Q0rZeWMdRb9Gr9qGZ/3zGKEi4pmjMU+8jUj57g7l6dAtfLHiA88RI9rosxkooH7yNbjMWpyL/kjm/+YSmnUb6wZ8jZueW0ksYXHRtMHkB3vbrqftx+M08DgNciyLmnCcvXVRqsNxaiNxfC4Dn8uBQw0JehQpUQRSyuMb/hdCzAe+rZSAolOI1eNaeA+OlU9gudOpn/AN4jkTcGcUQgcVgRarw2ziRG5xRKFpMOFrhAfPxP3BLwjO/zEJniCPb3PstCPJDfRdJdDU0WxHFEEsYVEbjVMbSVAXrSPb7yTb50JXCqFHoFaaUvQZnLsXkfnkPJwr/0Ns4NGEpn+feO5EUhHsbsXqqNmxktUF5/B7LuNIS/KK44d8KfQu9IKFHjsbp6GR6XWSG3DhcxmU1kbZWBaioi7KoS58ef31V3PuuV/tIknhjjtu59hjD7pOW58i5fMIpJSzUy2DopcTDRFYdCfelY+SSBtG/ekPE6/dm1KRTAsW76jjji0jqI4PYuSQDL7KB5y4/ucMq/iIj0b9iIgzo1tk0Qyd+rgJQCKWoGlgUdNz7Ulz2HlpGrnpHnLiCUpqoxRVR6gIxcgNuEjzONpl+77ssm9RXx8+aDpF+0m5IlAoOoJz5wKC7/8QvXoHdUdeQeiYH+GjBlpY/6c7qQwn+P3W4VTGHdxcuJVcn4+9sx5iywePM33bQwyoXs78MT8DzuhyWerjJu+utX0bbodBJH5gtEnTc+1J05G8vnJEAelOg8EZXkLROGW19hLZZSGdTK+TdK+zzQ11pk8/ph21VhwKShEoeiVatBb/J3fgXf1v4unDqfzas8QHzrC/jNWkVLa6uMY1r5RRHHVx07CtjPbZe2l4XTquSeexvnAyhZ/ewWmrvktd/CNqRt3Aqqr+s6ibBdQ08iNk+pxoaFTWxyiqiVBSGyHd6yTD68Tj0NUKqN2AUgSKXodz+wcE378ZPbSHusnXEJrxA3D2jIY0ZsL/Lc9hTXmMG4ZsZ3wgtO87PV5P7eo3qAXKcs9luPEBg9c9zVE7FlE8+leUBiekTvAUogFBj4Ogx0E4fmDoqcehk+51ku5x4EjOR7j++qspKtrDM8+8DEA0GuXBB//Mxx9/SFlZCZmZWcyadTxXXXUtaWlpbZa9bt1aHn74flatWonf7+ecc85v5rO4447bef31V/j442Vtnr/jjtv54ovl3HbbL7n33j+wZctmBgwYwPnnX8hZZ53bSXera1CKQNFr0CLV+Bf8Eu/a/xHPHEXl2c8TH3BUqsXah2nBT1dms6DMy6/mZDKsdGWraS3dweack3APOoL0TS9x1vIr+HToFTDy5m6UuOfQONrI5zTIDbipDsepDscoTo4SAm4HaR4HFvaoIm5a6JrGvff+nrfffoPzzruQQYMGsXnzJp599il27tzOvff+pdUyN2/exA03XE0wmMbll19BLBbjf/97nGg01uo1B6OqqoqbbrqBY46Zxamnns4HH7zPXXf9lpqaGi655JuHnW9XoxSBolfg3/oanvk/RasrIzr5cmJTr8brDZBoEtcPzWP7uwPLgj+sy+C1PX5uHFPJOeMGs6wda8dF0kdSfPq/8C+6h+nb/0r01U8wjvgRkeBQQpqXNeX91CyiaRgaZHqdxN0WdVF7CYuacJxwLEHCtNhbFyXT5+Ktt17ntNPO4JprvrPvcq/Xx+LFC6mrq8Pna3n9pX/842FA48EH/05+/gAA5syZyze/edFhi11bW8N5513IjTfeBMBZZ53LjTdey6OPPsKZZ55z0BFKqlDho4oejRauJPjO9/C9ejUWGnVTriOSMRZz84fo4UrM9W83+9MS3a0G4G+b03h8WxoXDKnmwiH2vsLtxeFwssozjbX5Z+LYu4lx711O9oe3EIhXdpm8vQmHbi9XMTzbt8+fYFkWpbVRtu+tIzsnj3fffZvXXnuZmhrbP3TVVdfyyCP/alUJmKbJ4sWLmDlz1j4lADBsWCEzZnTMGX3JJZfv+98wDM477wIikQjLli3pUL5diVIEip6JZeHe8BJZT8zBvf55olOuoO6oGzDTBqdasmY8v9PPnzdk8KX0vZySto31xdWHHBsPUBocT9Hk71EaGMuwvQsY/96ljCx5CyyzC6TuhWgaHoeOw9AwksrBsuDrV9xIPGHym9/8gtNPn8t1113Jk0/+h9ra5luINlBVVUV9fR2DBjV/n4YOLTxsEdPS0snKOnBF0MGDhwJQVNSxCY1diTINKXocevV2gh/cgmv7fGJ5k6j96r9xZeSmPCS0Jd7dUs/tq7I4JrueK/N3dXgxNdMVZOOAMyiuO5JRtUuYK2+lrvxJduZ/ix2ZX0rJ5Lieit9lMCDNw8A5x3HU5Gf56OMPWb50IWtWLOOLL+7lv/97ggcffowBudnNIo8aPkcizecjtFeJt7Tom9PZvEltSKfrPbffrRSBoueQiJG24n5cS+4H3SDypR8SH38eLt1Iid3/YHxa4eZ7n5YzIT3KnUeUsqOs82YMV/oKWTftKirXfcixu/7Gqau/R6lf8PmQb7I1Z3anldPbiUajbNy4ntzcPC486wzOPv10quujPPv0E/zv0Qd59pVXmXvaOfhdBj6XvWeC26GTnp6O3+9n584dzfLcvXvnAZ8bGvBoNIrLtX+bzvLy8mbXVlRUNPNL7Nhhl9EwMuiJ9FwVpeg/WBaure+S+eQ83AvvJp45itBRNxJ1ZWNufC9ldv+2WF/j5IbPchkYdPCXo0rxObpg2QjNYEP+achz32f+6NtwmvV8ed2PuXT5+Qxb/w9mZFQxPauO6Vl1uPrpL7myuorvXPstHn3sH9SEYzgdOh6nwZETJwIQcDtJ8ziIxE2KayJsLq9jfWmI3dURZs46gcWLF7J586Z9+e3Zs5uFCxccUEaDqWfDhvX7zpWUFLNq1RfN5LEsi+eee2rf53g8ztNPP0EgEGDatBmdWvfORI0IFCnFKF9LYMGvcO34kHj6cMIn30ssEkm1WG2yudbB1Uvz8Bom/zwjh8ySzdR1wTYAOX6D6dSRp0Ns/Fw2j5vD0PIP8Cx9kPSPf8l4w8+e9KnsSZuCZ9I5nS9ALyAnJ5cTTvwyr738HOFwPZMmTaasvIJXX3qWjMwsZp84l5ygm1A4RsKyiMYtInGT2nCcuWdfxqJFC/jO9VdzznkX4nY6ePaZJ/H5fESj+zseJ530ZR5//FFuv/0Wzj//IqLRCM8++xS5uXns2LG9mUyPPvoIRUV7GD58BO+99w4rV37Bj398Gx6PpztvzSGhFIEiJWh1paQt/i3OtU+DK0Bk5k3Ex5+HwwDWz0+1eK2yLeTgiqV5WMCfpxaTFyikZrdFV7hznWaY2tXvEssLUltiR8PEpp3EmqII41xFGNs+pLDiI4bu/YTV4+aix+tp6SftdeicNC7fztOhE4sfKG3Tc+1J05G8vE6DaKLz7tj13/sxAwoG8dH8d/ho/ru4PR4mTZnGJZdfQ3r6/vWcDE3D69TwOnV8bgfp3kHc9tu/8MQ/HuDJ//4bp9PJaaefiUPX+fe//7nvulGjRvPLX97Jo48+wgMP3EdeXj7f+MblhMNhHnjgvmby3HPP/dx115289torFBYO5447/sAJJ8zptPp2BUoRKLqXeBjvikfwfXo/Wrye2MCZ9mYxTh9smt9pG8V0Bdur4lyxNI+4qXHzsE1EaiJU1UXZUFTNiLzD27z+sNA0Iplj2BwrwBstZ1DlUvREGE/lBhy6l7AznYgjDbA3NrcSJp6kr9TvNAg1aYSbnmtPmo7k5YQO+XvuvOvASWJut5uLL72Siy+9Er/H2Wy/5ZbQNA23Q2f8qBH84o67qIvGqYuaWFhkep1cedV1B6SfM2cuc+bMbZbPRRdd0uzckUdO5l//evIQa5ValCJQdA9mArd8Fv+SuzFqdxEp/DKJGdeQKNmQasnaxe56g2++UEo4oXH/1GKsUM8wX9W7stmYdwpBTy5RfwZ6fQWBSBH+SDFm1IepB4gZPuJ6zzVLpBpDg6DbQX7QQXkoyt66GDWROAPTPfhd/aOJ7B+1VKQOyyKw+QXci+5Br9xCInc89Sf8FHPQDHu/4F6gCIrCBt9akk+tafK3aSUU+mPI0MGv6050XUfzpBN1BoklIjhitThiNQSSM68tdEyHF0wfmu4lZHmxtH7qYW4FTbfNRobuoqo+xvaKenKDbrJ9zj6/8J1SBIquwbJwbX8f35J7cJYsJ+HL3bdbGKEqWP92jzYDNbC73uCKJXlURnX+cVYuhVVbu8Qf0FE0LKxINYmYLV0cJ76MQupr9mJYMQwzipGIYtTV4cXCCyQ0FzHDi275cJpu4oYHi77d4LUHl6GR7XdRF0tQUhMhGjcZkObu0/ssK0Wg6FwSUdzrX8C3/GEcFZJEYCCR439GFDfoRqqlOyTKIjpXL82jLKLzw8ItDA0OQMpu9gd0EEsziGvGPtOQ26Gh6QZWLIyRCOOO16GFqrCj4zVMww1xH4bhJeHwktCchLsgIqqno2tQkObBZUQpD0WJmxYF6W4MTW910uCtt97Orbfe3q1ydhYpUQRCCCfwD6AQcAO/llK+lApZFJ2DFqnGs/pxfF88gh4qwcwaRWT2L4iPPLnHRwK1REVU58qleRSHDW4atmXfngK9Hk0Dp5dINA56AFwB3IZtWkrUV+GwYhjhvbipAMA03Dh0P7oWRLfcmFrvUuYdwcQeHaR5HFSH4+zcazI409cnRwapGhF8AyiXUl4ihMgClgNKEfRC9JrdeFc8gmfNE+ixWhKDZlA38nQSmaMBrcdHArXE3nCCq5bmsavOwT2TSwhE+4gSaA1dx3L5icZMooDboROPhjHMKA7NxBOrRItV4Abiuoeo4Sfm8KNZPXMlzc7G77KVX3U4TllthIK0vud4T5UieBp4Jvm/BvTDwWfvxlu5CveyB3FseBUsi8SIuUQmXYKeM4pEL+v9N6YqpnH1i2VsDTm5f2oJkzIiyKJUS9XNaJDQXfafN41QXCdoxEjUV+NMhPDGKvDFyqFew+XwknD4MR0+Yo6WV/rsC/hdRnLp6xh+l0Gax5lqkTqVlCgCKWUtgBAiiK0QfpoKORSHjqN0Fd5P78e96VXQDWIF04kOOR7LkwkVO3Fn9tz1VA5GdUzjmqV5bKiN8YcjS5iUEe6RjuHuxKFr+B0mhuHE9GUSJxM0k0RtOS6iEI/gitsbL7g1A9MZIKH5iBl+EnrfaiyDHgemBburIrgdOm5H3zGTpcxZLIQYAjwPPCClfCJVcijah1G6Gv+Su3BvfRvTFSQ2+XKivkFYrkCqResUqmMa1yzLQ9a4uPPEdLKKVyKL6FWO4a5AwyRRX4PDqe+LSHL6g8R1N4bTS0Q30SwTw4zgMiz0WB1BswoAU3dixYPE8RE1fFi93L+gAQXpHrZV1LGrKsyQTB8aoGtah1edTTWpchbnA28B10sp302FDIr2YezdSHDJ73BufB3LFSQ67TpiE8/H4fJg9WITUGMalMC6ahe/PbKUWUPy2dB84zNFK1iaTtzw4vAHSURCxGJRDDOCw4xiRKpIsyoAzfYvOPwYehoarl4ZqqrrGkG3g8r6GMXVYfwug6DH2esdyKkaEdwCZAK3CSFuS577ipSyPkXyKJqgV+/Av/Re3PIZMNxEhp1IdPBx9ibxWxf1Ogdwa1RHzH1K4N4pZczIqu/35qAOoYGpOTANBzHDjy+YTixcjx4PYcRC+KLlEC3DhU7M8BIzbDMSlgOPA4zk3XdaMfwO+/8Eeo8KYfU6depjOrWROB5n35iUlyofwY3AjakoW9E2eqgI37I/41nzBGg69UdeiXXk+Zg7lqVatE6nIqpz/Uul+5TA7Lz6LllFtF9jWcRiUcAJjgw0h4lHi2NZJnqsHn+0FCjFCuuYhpc4OgndjeV0k0guVmd40+hpK+aneRyUhaLUhONkeF0Hv6CHoyaUKQDQ6itI++yPOFf+B8w48bFnEZt6JZY/r0duCtNRtoYcXLcsl5JojN8dWcqMLFsJqNFA12IvdeEh7ggQcuroVgJnog4f9ejRWtxmBKjBiuroupOE5sSKOdC0ro1IqtxbgZ6RBlr7mkSHruF3OaiNxLnhhqspLS7imWde7lIZuxKlCPo5WqQa7/KH8a54BC1eTzxvMpHCk7C82bBrhZ2mj5iBGvio1MNPvshGB/5ySiaubSv3hYiOyAvSu12azQm4TPS4PRfCgYaWiODS9y+a1/ScAw23bm+040zEcOmRg55rOG8RPfBcPIZDP3CBPt3hpyb5v6kZRBxBHJ4siEcw6/ZimFFcWhwtHsFt1kKoFjcaVr0bXXOT0N37wlu1NjS3ZpnoVgIjFsETD6ObMQwzhm7FcNTF8JoxNCw++WwVP7/3YR69/26yBo6gVgtAO/wXAZdBfTRBLNEFmxJ1M0oR9FM8sVLcy/+Oc8W/0CLVxIefhDntSiKl21ItWpcRM+HBjen8bXM6Y4L29pIDcwvY0HerDIAeryMh37T/NzQw3CSi+/fqbXpONzQSycZNd3lIRMMHPddwnkTkgHOG20sicqDrzzH+FByujH0+ALB9ApYOEc0gbngxnLodkYSJ2+WCWD2GGcYdC6HFq/ZnVgde7JGGpWloIfBaFmChsV8Od/Joag57lOH0kcDAjEf4YuN2akJ1aJFavKGduHUXUW8ehhVs00+haRDwGJimhdnOfY57KkoR9DcSEbyrHsf36Z/Q68uJZwkiEy/DDA7CnTYI+qgiWFMW5UcL85E1bs4YWMNNYi8uo3f/eHszDWGpDTicOprL3yydhQ5OL+FoHLfLTUQL2srB7cVMxHCQwEzEwTIBC0PXsLc/0LB0A0szcDhdRE0DS3fYrTfgNHQsyyQaqiah22oi5s0lHhiAHirGE9qJGfESMdKx0Fr1U3idBrpml2laVq+NHlKKoL+QiOGRT+Nb+keM2t0kCo4iNPZ8zPRhqZasS4mZ8NdN6TyypQS/rnPj0G0clVbNttK+aQbqD1z8zW9xzNEzGTtmNE8+8xS7du8hNzeXs888kwsvuJhwbbWd0ISVq1bxxFNPsmr1agDGjhnDJRdfzPQpk9Bcfn5/zz28/e47AHz9ssuZPGkyf/jNHbjiIdzxWryJBPWuzH1lf7psMX9/5CG2bNpIZlYW511wGU5DA+xZx9l+F9dffzVFRXua+Qyanr/++qsxDAfnnXcBDz30Z3bv3s2wYcO47LIrmD37pK6/kY3oWa54RedjxnGve5qsJ2YTfP9mTH8+lWf8j/DpD/d5JbB8r4uvfzKAhzalc+ooH3eO2sBRadWpFkvRCSxasogHHn6I2ccdx7VXXY3H7eH+Bx/kk0UL96X5ZNEifvCTH1NcXMzFF1zIxRdcSElpKTffcgsfL7TTnf6VrzBrpu0Du/7b13DpJZcCGlFHgLgnC92K4YuWo5lxln+2lB//8EZCoRDfuPxqjjthLn994F42bFiHpmmUhaLED3ELzq1bN3PbbT9i8uSpXHvtDWiazk9/+iPeeuuNTrtX7UGNCPoqZgL3hhcILL0XvWoriWxB+OR7SQw9Dqem9clIoAb2hhP8flUWz+4MkO+Jc//UEk48ZirLPkykWjRFJ1FSUsJDf76fcWNGEomZzJo5kwsuvYS33n6LyRP/v73zDo+qSh//505J7yH0EiBw6ARQKSooUizYy3fVn72vrquuFbFgAXUBG2thVVx17V2KggsiIBZQasihJKF3ElJmkmn398edxARCMkmmJufzPHky995zz3nvzDvznva+b1/cbjczX32F9EZ61oUAACAASURBVPR03pz1JppufPYTzjqLm/56G8+//C+Gn3wafXr3plvXTJav+IlTRwynfefulJcZU1YeSwwOaxqxzkKiS3fynzdfIT29FdNemEVcvDGNlT34RB6+/28kJ6Xg8ejsL2vYt+rQoYPceec9XHbZFQCcd94FXHPN5bzyyouMGTMOkyk4fXU1ImhueNxEb/qS1A9Gk/T939EtUdj7XoWt37U4KyrwbP4ez6aFaO7mZwY8Ony+M57x/93Hl7vi+X9djvDhsN2cmGZHrQY0Lzp36ky3rl2rjtPS0khNSeHwYSN89uYtWzhw8CDnT5hAfPyfaw8JCQmcP+FcDhw8SK7MrbcdtykKuzWVwsMH2bJZMvqMcVVGAGBA9hC6de+Bjk5slJkjNieeBihbQkICF154adVxdHQMF1xwMQcPHkDKjb5X1ETUiKC54HGRsOVTole+gqkwD09qd8rHPIfW9RRcm38MtXQBRxZbeTInjTVF0QxpZ+Hi+Fw6xlSw46BxfXAPZQqaEykpKcecs1qteDxGz3/vPmM/cKcOHY8p17lTJ6PM3r1kZdY/Peo2RbGj1FgE7pwWe2x9nbuQk7OexGgL5U43zgZMD7Vv3xGrtWZwvo4djcCNe/bsoXfvvj7X1RSUIYh0nHZiNn5I3OpZmEt24I5rjb3PFbgy+oHLQ7SnebvKHqow8a8tyXy+M4Eki4fH+hzk0lED+X1peCSXVwSG+nII17Wbs3Kr59E/wHXWZzVGAU57MYnluymJaX9MfSYN4qMtuD06ei0CeDzHGojaZKgsZzYHb8JGGYIIRbMdIHb9u8Sum42pvBBn2xNwDr8bZ0U5tICk5OVujdmrinn9t/aUezROTz3ExW32EW/yAANDLZ4ixLRp0waA7Tt3HnNtp/dc69atfa6vQ7t2aJrGtgMlxLiOYHaaKI9tS7kL9uzeVVUuPsqM2Wym3OHA49ExVQtLevjwoWPq3bNnN7qu1zBsO3duB/4cGQSD5v+L0ZzQday7fiLlu5tI/8+JxP82A0/r/tjPewPHea+jZY5o9kagwg0fb0/g3KXtmPFzMSell/PBsN1c3X4P8WYVIEJh0DMri7S0NL6ZO4eysrKq82U2G1/Pm0t6WhqipwCoWpCtrRdfSWpKEv379WPBoh/YW+LEWlFIdPlBcnPWsXnTn2sNGtAmoxXFR4rI3fangcjN3cjOnTuOqffw4UMsWrSw6thut/PFF5/RsWNnunfPavTzNxQ1IogAtLL9xGz5mpgN72Ep3IIelYiz/VAc7Yehx2VA8UEoXtjsQkFUp6TCwzt5SbxXkMghh5m+SRU8M74tvctW4QE2l9RbhaIFYbFYuP2WW3n62We44eYbGD92LADzv/uOQ4cO8eQjk6oMQHJyMgAffPIpw4afwgnZtY8ob7nhRu554H5uvf8xLjrrDBz2Uj6Y8z+Sk2uuV4wbdyY/LFrAE5Pu4cILL6GsuIjPPvuIjh0743I5j5FzypTJSJlLq1YZzJ37NQcO7OO5517w91tSJ8oQhClaeSHRefOJ3fwF5l2/oOke3Bl9qRj1GHQ/HUfez6EWMeDoOuQUW/lyVwLf/G8PZc4UhqXbuSlxD73jy+iZ2gm5tbjFJ4+pD48lDrMYDxjhIzRzFB7Hn2soR58zmTXM3jAR5qhoPI6Kes9VntfdjhrnTFHRmB0112s8QUppOfKUU0hIeIoPP/mY995/H7PFQi8h+Mff7+KEQQOqyp0+chTLli9n/ncLWL12PW+8+mqt9fXs0YOXpj3Hq2+8xVsffk5SQhw3XnYuOdv2s2ZDTlW5ESNO5e933c+HH73PqzOfp2Onztzzj4dYs3oVP/20rEadrVplcOed/+Bf/3qBQ4cOIkRvXnjhFbKzBwfmTTkOyhCEEabi7URtW0RUwf+I2rkUzePCk9QJR+fTcLUeiCfemPeMbubTP/vLzczfE8dXu+LZXBpFlElnXPdYLkvdQo8kJ5v3ltVfiaKKUocJMDLJRZtNaOZ4yj1/DqGOPhdtNlHhXbCMMSdS7imp91zled1dVuNcnCUJW3lNJ75olwmtCZGb35v9NjHxiVX7/aufj0tIwlb6Z3uDs7M55ZSRNc4dTUJCAs8+PYVob5iLo+utTm8h+OeUqQCYYxKxlO7icncZZVGtsXnLeIAxZ1/AaWeez6EyByZNo0taHGeMHsM99zxwTJ0jR57GyJGn+fz8gUAZglDitGHdu5Ko7UuI3vY95sKtAHiSOuLsdznurPGY0rvj2LwkxIIGFo8OOcVRLNkfy9JV+9hwoAMAfZMquLb9LoYmF5E99HQ2rzyEO0H1/hVhgqZxJLYTqc59xFfsx+xxUBrdpuqyxaSRGhfFYZuDXUfsdE6JxRLEnUANQRmCIKI5SrDsW4111wqidq/Asm81mseJborC024Q5Wm9caX3Qo9rZdwQ4cngj8cRp8aGI9FsOFzMqrwM1hZFU+IyYUInu63G7VmFnJpho0u8i817VUgIRXhiMWnEW3T06LY4NTMx5YeIdpfiNnegDGP6K8qskRJr5YjdSf5hG51SYomxhl+Eq1DlLDYBr2Ds86sAbpRSbgmFLAHBacNcshPzkQIsB3OwHNyA5WAO5mIjsqeumfFk9MbZ/wo87U/A3TYbi9WKs5nkAAYodWnstlvYYzez31bK1m2pbCm1kldq4UCFoXYaxXSLNzO6TRmDUioYmm4npdsQNq9chaME3LGq968IXyojqFqsJip0K86odKKdR7AUbyPFFIs9KpUKSxIxFhOJqbHsLiqn4LCNtPgoWsWFV1azUI0ILgBipJTDhRDDgOnA+SGSpXZ0HdzlaC7jD1c5WrVjraIIk+0QJvtBTPYDmGwHMZXswlyyA5P9z/3COhp6cic8rXriEBPwtOqF1qYPjoLfjAL2MshfHrY7fjw62JweiipM2NwadrcJm0vDVmBn/854Cp0mCh1mCh3G/32/7WPXkY6UuKoPgYuINcfTNd7JSWnldI130iPRQZ/efdmzdnFVqUSL+uFXRC5uzYotKp04C2j2QpLKd6OzF6c5DlNMAlmJMRwo1ygsLafI5mTSlJdIjDEc0Mym0IavDpUhOAX4FkBK+bMQ4oRANGIu3Er8z1ONH3KPG3Q3eNxouqvqWPMYscw1V3mNH37N7btnqic6GT02DeJb4+4yEldiezyJ7TEltqHi0C6wRP9Z2F5GtPlYb8LpK46wcUcGADqGHdIBU84B3GXVzgPa+gN4bK2rjvGW19bux21vXXUMoK3ej8veBg/g1jXcuvHj7vllL66Kdrh1DY+OcX7JbtyuDrgBt8coW+4xAbuBo131DwHpAFg1nZQoN6lWD+3SzfSPK6NNtIu2MS7axrrI6jOYgpULqK7r3VITMUeF53ypQtF4NIhNwWFNxuW0YXaVYnHaMJUZIS86AB1MRgfRXW7CbTfhxoRb06jKiqZpmDSNaIvJmD1IaAvm6OO26Bep63KiCBRCiDeAz6SU873H24FuUsrjxUM4ADTPjCkKhR9Yv35D//btM5t3PBFFDXbvLrD069d3XT3FugAZ9dUVqhFBMVB9HsBUhxEAHx5EoWjJOJ3OApfLfbC+GDyK5oGu6zidzlaAX2ZTQjU2Xw6cDeBdI6jPqikUijrQNArdbpfvUdQUEY3b7bJqGoX+qi9UhuALoFwI8RPwPHB3iORQKJoFuq7Pt9lK1Wp7C8FmK03UdX2+v+oLydSQlNID3BqKthWK5ojH45lVUnL4TCAtLi6hxGy2ONU0UfNC13XcbpfVZitNLCk5XOTxeGb5q+6QLBYrFAr/s2rVqkyTyXSzpmln6Tqp9d+hiDQ0jUJd1+d7PJ5ZQ4YMKfBbvXUZAiFEPtTI8ucErECFlLK3v4RQKIKJ0muFoib1rRH0AvoAi4G/SCkFcDGwrM67FIrwRum1QlENn6aGhBA/SClPq3b8o5RyZCAFCxb1hbsQQtwE3AK4gKeklHNCImgj8CWUhxAiA2MX1wApZXnwpWwcPnxudwN/8R7Ok1JOrqWOsNRrH57tduBajFHNNCnlx6GQs7H4qJcmYC7wlZTyteBL2Th8+OxexHCorQxxer6U8kjQBT0KX3cNFQkhnhRCnCuEmArsCaRQQaYq3AXwIEa4CwCEEG2BO4GTgfHAVCFEYF38/Mtxnw1ACDEeWAC0DYFsTaWuz60bcCUwAhgGjBNCDKiljnDV67qerRVwG8aznQFMF0JE2qpwnXrp5SmIyHWO+p5tCDBeSnma9y/kRgB8NwRXA0XABGCv97i5UCPcBTUdNE4ClkspK7wf2Bagth+UcKWuZwNv6HTgcJDl8gd1PdsO4EwppVtKqWPM/9c22glXvT7us0kpDwLZUkonhgEv9z5jJFGnXgohLsHQzW+DL1qTOe6zeUcLPYBZQojlQojrQyPisfi6ffRTKeW4gEoSOpKA6lbZLYSweD2dj75WAiQHU7gmUtezIaVcCCCECIVsTeW4z+b9kTzo7Sn/E/hDSrmpljrCVa/r+9xcQog7gMnAS6EQsIkc9/mEEP2AK4BLgEdDIl3TqOuziwdeBmYAZmCxEGKllHJtCOSsga+GoFAIcR6wCcNSc5wvViRSV7iLo68lYvQgI4WGhvKIJOp8NiFEDPAWhvH+63HqCFe9rvdzk1LOFELMAuYLIU6XUi4mcqjr+a7GiM22CMgEHEKIAillpIwO6no2G/CilNIGIIRYhLGWEHJD4Oti8dFKpkspRwdGpGPxeDy62x1po19FuHPdddcC4PG4natWrVpOkPXaaFvptiJwWK3mg/gQq61RDmVCiCgppaMxgjUGp9OtFxXZ6i+oUDSCjIzEVcAJwdZrULqtCCyVul1fOZ+mhoQQtwD3YCy6aRgOOD2bImA4sGTJIhYtWtioe4uKjBmilJSURt0/evRYRo0KauezyTS39+vLLz/jo4/+i8NR0X/fvn35NBO9bir79+9j8+ZNnHzyqaEWJSLweDwUFh4mPb1VqEVpNL7uGrodOA2YD1wH5ARKoEihqOgwRUWRuNkmNITj+/XFF5/w8suzGDp06BGUXlcxc+bzvPDCczgcQR0cRSRut5uXX57Bbbddz4oVkeuP6Oti8W4p5R4hRKKU8gchxGMBlSpIjBo1utG9zMceewiAyZOn+lOksKa5vV/p6Rm0atUKm81mak563RR0XWfjxg0AbN26md69+4ZYovBD13X27dvLL7/8xMIF89i3fz8AM2Y8S3b2Ak47bQzZ2UOIj48PsaS+46shOCKEuADQvdNEkTsGUii8JCQk8OOPP6BpGkqvoaSkmA8+eLfq+IUXp3HH7XfRr98AWnIkU6fTyfbtBWzevInNmyUbN6zlwCEjL3n3ZDfWeJ04i86AdCffb/yDF1b/gaZpdOvajV69+9GjR0+ysnrSunWbsH0ffTUENwLdgYeAfwB/C5hECkWQePDBSezatZOHHnpo18KFC3vSAvXa7XaTm5vDkiWLWf7TUhwV5XiscejWWA6XlvPEE5Po2rU7o0ePZdiwEaSkRKKzb8MoKysjJ2cdGzasQ+bmUFCQj8vtBiApGnomVXBWTycD0p20jvUw5fckAM7LtDOhi53NRyxsOGwl57BkwbdbmTvXqDcxPp6snr3o3bsvffr0JyurB2azOVSPWQNfDUEfYKiU8iVv2IXiAMqkUASF/Px8cnLWM2LECZVeui1Gr/Pz8/jhh/+xdNkSSoqPoJmtOFIzcfToR/S2FQCU9D4H64FN5O3bRP6br/HWW6/Tt29/Ro0azbBhJxMTExPip/AfHo+HVat+4/vvv2XNmj9wu91YzdAt0cW4Dk66JbnoluQiPdpDXZ16kwYixYVIcXERdlwe2FlmJq/YQn5xOVs2/8Yff6wCIDkpkWHDR3LmmefQsWOnID1p7fhqCGbyZwCvR4C3gZAH51IomsLzzz/H5MlTKg8bpddCiGsxopm+BnwopRx2nHLZwHlSyicaK68/KCkpZubMF/j999/AZMaZ3BFX90G4kjuB+ahMlyYLzjZ9cLbpg8l2GMvhfNZvyWf9+hd4593Z3HjDLYwYEfk7izZtymXW6zPZtn0bqTEwrr2dQa0cdE92YW1iDkeLCTIT3WQmuqFDBVBGsUMjp9DKygMVLFo4lwUL5jJy5GiuueYGEhOT/PJMDZbTx3JOKeVWACllnhDCE0CZFIqgYLFY6NChIxB4vZZSrgZWB6p+X3nrrVn8/scqKjoMwdG6F1h8i6HoiUvDEZeGo8NgzCV78ez8jRde+CfdumXRtm27AEsdOJYvX8rLL00jJdrNrX1KGdragTnACXyTonSGtXEwrI2DYkcZ87fH8u3SRWzMWcfjk58hI6N1YAWoBV8NwTYhxBRgBUYgtl2BE0mhCA5t2rTl9df/RffuXbIef/zxXRhhg4+LN77PRRgxYw4CF/ralhDiNOBWKeVfqtezfn1wd6yWlBSjRyfgaDeAOuc4joem4U5qhyOjFzEFy7DZyvwvZJDYti2fmS9PJyvJwd0DiomzBN/DOylK5/+ybJyQ4eC5NTB92tM8PWVG0NcOfLV91wH7gbO9/8Mmap5C0VgmTnyM1NRUVq5c6QaeATQhRK0eb97IkenAGCnlUIxO1IkNbbOWeoLK0KHD0cqLMdkLm1SPpXAb6a0yyMzs5ifJgovL5eLVV14kzuLmzn6hMQLV6Z7s4oZeJWzNy+Prrz8Pevu+GoKBgEdKeRtGfPc+dRUWQpiEEK8JIVYIIX4QQmQdp8x8IYRKYq8ICVu2bEbTTEyfPj0fQ68dQK0roFJKj/f6B0KIN4GOGJ72DaKWeoJKt249ADBVlNRTsm4szlKyumdhMgV4HiUA6LrOW2+9zta8rVzdo4TEqPCI9XRihoMTMxx89OF7rFnze1DbDtRicVVyBiHEMIzkDOcfVSZSE09ENLNn/5uCgrygt1vZZqVjWbDJzOzGddfdVONcLYvFx40C6U1scwHwBzAIIzJmh1rK1ZlZq7IeKeVQIUQcQR5d79y5HQCPNa5J9bgtsWzfsR1d18N2b3xt6LrOO++8xcKF33JOZzsntW6a97SuQ2GFCbtLY9GuaE5vX9GoGTcwZupu6F3K3t8tPPfsU0x8eDJ9+/Zvkny+EqjF4hrJGYQQzSnxRERTUJBHwaa1dE5wB7XdZIxvh2f3H0FtF2B7ae3zrbUsFtdVzRYgDmNdYAuwESP+1stHlauvg7MFKBNCLPdFdn/icrn47PNP0GNT8MQ3zXfOkd6dPflL+fnn5QwffoqfJAwsuq7z/vvvMGfOl4zpaOey7k0P9rdoVzT77IZ+vS0T0HU4o2OdS011EmfReSC7iKmrU5g65TEmPfIUvXrVOQHjFwK1WNycE09EPJ0T3Ewc3GK2zFc5/BxN5WLxwIF9k++9994nMXrxe2srK6W0CSEWAr9KKT8EEELsklK+XVlGCDENyKb2Do4VcHhj0VfF6XA6gxeDes6cL9mzeyf2HmMat1BcDVd6d/R9Obw1+w2yswcTG9u0EUagcblcvPfebObO/ZrT25dzVQ9bU98CAP44FHXMcVMMARgLyA8MLGLK6lSefupR7r7nQQYPrjeAaJPw1RBcB9yKsVicAzxZT/nmnHhC0UyYOPExvvrqM5YsWZKMsQniSW+imntqKf4ivnVw1mB8XxxCiMrp1CEYI+Cac1NBZMeO7Xz40X9xpXTGndK56RVqJmxdhqPlzuXtt9/kttvC1yl79+5dvPD8s+QX5DOuo50r/GQEABxurc7jxpISrfNQdiHT16Ywdepkxo07m2uuuYGoqKj6b24EvhoCF/AL8DtGGOqLgQ/qKL8cOBf42LtGsK7ygpTy/srXQojHgb3KCChCgdlspk+ffvTr1+vwN998sxa4WEr5AfB1beWFEKdSfwcnrdr1Z8JBtx0OBzNmPItbs1KeOcJv9XoSWlPRpj+LFi0gO3sww4ef7Le6/cX27dt47NH7wWnjzv4lnJARORFVU6N1Hh1cyCd5cXy3YB579uzkoYcex2pt8B6FevHVEHyBMbTtgJFrczd1G4IvgLFCiJ8wDMd1Qoh7gC1Sylq/ZApFsJk48T5cLhf79u3uDLxK/XodkR2cjz76Lzt3bsfWYyx6ExeJj8bRYTDWkj289vpMevfuE1axiLZtK+CpJx/G6rYxcXAhbeIizw82ygxX9rDRKd7NG+vWMm3aFO6550Gio31zBPQVX/d+tZJSnokxKhjCcbbYVSKl9Egpb5VSjpBSDpdS5kopZxxtBKSUj9e2s0KhCAZHjhQxY8bL9OnTpwwf9Bqjg1Pu7eA8D9wthLjHO50UlmzduoVv5nyJo1VP3CkBiGdjMmHveio2u5233prl//obiZS5PPrI/WgVxdw/sCgijUB1Rrav4Jqepfzx+0qeenISNpt/s9r5OiKobDVeSmkXQoTHxltFgykqKqSwxHzcBdTmyLYSM6lFxzpQVQZNs9vtJl/02usDcLTfS24t5R5vtLB+xOl0MnPm8+iWWCo6Ndj3zWc8sSlUtBvIihXL+OWXUxk61H/TT40hPz+Pp596hCSTnQeyi2gVG9lGoJIzOlaQYNV5LSeXZ6Y+zqRHnvLbmoGvhuBzIcSjwBohxM9AqV9a9wNqX7yisYwceTqzZ/+bnj172sNNr/3BnDlfeqeExvgcUwhdR3PY0NwOrPtzcWYIn3YYOdoOIKpoG/9+4zX6988mLi40u4gOHjzA1KcfJZZyHswuIj0msEbA7tKIiYlhwoQJzJkzB7vLVf9NTWBoGwc6JbyyYSMzZ87grrvu94tTn0+GQEr5r8rXQoi5wOYmt+wnCgry2CA34Y5Lq7+wH9E8xoLN2h0Hg9ougNnW+JSPKSmpJNkKWtz2UVMtc9cXX3wZABkZiXtnzZp1E2Gk102lsPAwn3z6YYN3CVkP5GKuMHQjZttPgI6zde/6bzSZsHUejrZxDl988QlXXnlNIyVvPDabjSlPP0p52REmDQ68EQCwuTQmTJjAHXfcga7r/Dj344C3OayNg0PlZXy0YjkZGf/hqquua3KddRoCIcQkKeVTQogPAP2oaw5gjpTy0yZL0UTccWnYe50dajGCRmzuvFCLENG8/fYbXHvtjTz22EQ0TcPtdnTFSLoUVnrdFL744lOcTiflnU5q0H2Woh3HHPtkCDB2ETnTujFv3jecffZ5pKYGd+H4o4/+y65dO7lv4BE6BclhMs6iM2fOHHRdZ+7cubQJUsyiszuXc6DczNdff85JJw1HiF5Nqq++EcE33v+1LehGAR8DEf2FUbQ8Tj7ZiI5ywQUXAxAVxYHFixe/7r0c8Xq9atWvfPvtHByteqLHNHAtyOOq+7geKjoMwrphGy+9PJ2HHnw0YPvea2PtmlUMSHPQNy2w0zPVibXolJeW89lnnxnHKcExBJoGf+lexqJdMaxbtzqwhkBKucb7f0lt14UQ4ZFnTaFoAD169ARg0KAhAGRkJJZW1/FI1utly5bw8sznccelU9HA0YA/0GOSsXcZwfp1S3l6yuPcf99E4uMTgta+OXLCHjUZkx+ftamrDKv8IoVCEV5EnF673W4+/vh9XnxxGq64DMp6jj8241iQcLXqgb3rSHJyNvDQxPvYvTs46Ut6ij6sPRzFxkJf98BELh4dPs0zFuR79KgzRpZPNPUdU9tII5DtpcHfPnrEYXRfkkMQ8nd7qZnMht0SUXq9Y8d2Xp/1L2RuDs70LMozTwZTaAc1rlZZ2KLi2Zu3mPvu+ztXXnk148efE9CEK1dccTUydwPProZzOts4P9NOVMSO7Y7PPpuJ2TKBnEIr48adzYAB2U2us/mbTkUNQpVI5Ih3u21q++C3n0nonjuQFBcf4dNPP+Tb7+aByYq966m40rOaHFDOX7iT2lHS53xiCpYxe/a/WbjwO66++nqyswcHJHR1cnIKT0+ZwezZs/hmySJW7I/lgi5ljGhbgSVAaROizHqdx/6kqEJj3vZYvt8VizUqmltuuYkzzhjnl/eyqYYgPDRO4TOh8j2o9LeYPHlqSNpvIGGt106nk3nzvubTTz+ivKIcR6ueODoMRrfGhlq0Y9Cj4rH3GIelcBs7d61kypTH6dd/INdecyNdumT6vb34+HjuuONuTj99DP95+9+8kZvPJ/kJjGxr49R2FbT1s4fxoHQHa6tFIB2U7t9YRh4dcgqtLNkdzcqD0Xh0jVGjRnPFFVeTmuq/LfNNNQSL/CKFQhFehK1er179O7P+/SoH9u/FldyJiqwT8MSGT3yfWtE0XGmZlKR0wro/l/UbV3PffXcyfvw5XH75VQFxPuvbtz/PPvciq1f/zrffzmHOH6v4ZlscPZJdnNK2nJNaO4i3Nr33PrpDBd/uiMXu0riom43T2zctBHUlu8rMLN8bzU/7YjlcDvFxsYw/cyxnnnkO7dq190sb1anPj2AFx86XaoDujSNUXzjqgFNUVIjZdqhF7a032w5RVNQMJz+DxC23XFdjOO12O3tt3LhxBWGk10fj8Xh4993ZzJnzJXpsCvae43EnH5Mgrem4HTU8ZUvdfuzhmsw42/bF2SqL6F3GD/TKVb/x8MTH6NjR/3GQNE1j0KAhDBo0hEOHDrF06WJ+WLyQ2XI3726GgekVDG/jIDvd0ei1BE2D1GgPqdGGUWgKh8tN/LI/ihX7YigoMWMyaQwcMIirTxvDiScODehW3PpGBH+p57pCEXFUS08JQGJiVN748ePDWte//vpz5sz5Ekfr3sa20AAtBmsuBxPO+9NT9uNvvvN/I5ZoKroMx5nWDfIWM/mJSbz4wqsBDUuRnp7OBRdcwvnnX0xe3laWLl3M8mU/sGp9MbEWOCmjnJHtK8hKcgV1icXhhpUHovhxTwwbC63oQLeu3bj24tGMGDEyaE559RmCW+q4NtGfgjSWlJRUtpe4W5xncTiF+400vvrq8xrHHo8jgz91PSz0+mi+/W4eQZ/+sQAAEVJJREFUrqT2VHQeFtDFYN0SVcNTVrcE7sfZk9gGW9dRIOfzxx8rqxz9AommaXTvnkX37llcddX1bNiwjh9/XMzPPy9jyR4HHRM8nNbOzvC2FST6YeroeOwoNfPjnmiW7Y2lzAltMjK45NIxnHrqaQGZ+qmP+gyBDIoUCkUQ6dy5S41jTXOVE+a6Hhsbi8lWCroHtABOC5qjKLcdrvKUJTElcG0BmqMMgJiY4C90m81mBgzIZsCAbG644RaWL1/KwgXzeG9zHh9ujWdwqwpGty+nd6p/RgnlLlixL5rFe2IpKDZjNps56aThjB17Jn379vdL8LjGUp9n8X8AhBAW4ESM5DQaUKfJEkKYgFeAgUAFcKOUcku163fz57TTPCnl5MY+gELRUM46awJg5LHNzc3BZHJVAAWEsV7/5f+uZNq0qcRu/p7ybqPQrfWlTgh/LIe2ErttOd2zepKdPTikssTGxjFmzHjGjBlPQUE+ixd/z49LvufX1TYyE91c0q2MAenORtXtcMOCnTHM2x5PqRO6dO7MtReN59RTR5GUlOznJ2kcgcpQdgEQI6Uc7s3kNB04H0AI0Q24EhiKkcd1mRDiCynl2sY9gkLROBqRoSxkej106AhuvfVv/Pvfr2LJ+QJ7x6G40rqGjc9AQ9AcNqK3/4y1sADRqw/33zcxoI5mDSUzsyvXXXcTV155DcuWLeHzzz5k2pr9nJDh4KqepaRG+z5ltOGwhdmbkthvMxauL7roMoToHRA/iqYQkAxlwCnAtwBSyp+BE6pd2wGcKaV0Syl1DANT3iCpFQo/0IgMZSHV6zPOGMczz8wgs0N7YvN+IG7Td2j2I/5sIrB4PFj3ridx/WfElOzkiiuuZvLjU8KmV3w0UVFRjB49ludfeI0rrriaNUVxPPhLGj/sjkavxxaUOTXe2BjPs6uTMSe245FHnmTixMfp1atP2BkBCFyGsiSguoa6hRAWKaVLSukEDgohNOCfwB9Syk0NE7smZtvhoG8f1Zx2gJA48Rj5CFoFvd3mRkMzlBFkva6NzMyuPDN1OgsWzOf9D97FvOELKtr0xdE+2z+xhUyWuo8bibl4D7E7fkazFZI9aAjXX38Lbdu280vdgcZqtXLhhZcybNjJvP7aS7yVs4HfD0ZxS+/SWn0RthyxMHNDMkUOjfPPv4hLL73c7zmG/U1DMpQ9gu8ZyoqBxGrHJillVWxYIUQM8BZQAvy1AfIeQ6hCB1RmKMvsFIA8sPXSqlmGTAg2I0eezttvv9GQDGVB0+u6MJvNnHXWBIYPP5n33nubJUsWEX14K/YOJ+BK796k6SJXSicsR3bWOG4KWkUp0Tt+xVpYQHp6BtffPpETTxwWlr3i+mjXrj2PPjaF+fPn8O67b/LUHxYeGFhUo8wfB63MXJ9EWnoGT9/zIFlZPUMkbcPwOUOZEEKTUureDGVbAIQQ50spv6rlluXAucDH3rnUdZUXvD2mr4BFUspnm/oAKmSCorFcfPFl6LpO69ZJlRnKwkavfSElJZU77ribcePO4s03Xycv70fch7ZgzzwFPbpxoZ+dGb2w7t2A5nbg6DDYSFXZGHQd6/6NxO5ahcWkcdH/Xcm5514Y9j3j+jCZTJxzznl06ZLJM1MfZ8baZKJMHkwa5BWbmbk+ic6Z3Xl40hMkJkZOXnCfx33eeU+klOuqnf47hvIfzRfAWCHETxi7jK4TQtyD8UUzA6OAaCHEWd7yD0kpVzRCfoWiSVT2TCNZr3v27MXUqdNZuPBb3nl3NpacL7F1Hm6MDhqKpqFHxaETh7N145KdaA4bMflLsRTvov/AQdxy8+20bt2mUXWFK/36DeCuux/k2WefJD0aWsV4mLUxmeTUdCY+PDmijAAEKOiclNID3HrU6dxqryN/75uiORNxem0ymRg//myyswfz0ssz2CSX4CzaSXnmiKDmJTAX7SCuYClWPFx7018ZO/bMiJwG8oUTTjiJESNOZcVPS7GaYK9d497bbgnbxe+6aKoHQ0TFbVeEDrvdTm5uDgUF+aEWxRciVq/btGnLE5OnctllVxBVmEdC7twqp62AoutY96wjbvNCOrdryz//+QLjxp3VbI1AJWeffS46sNduplV6OieeODTUIjWK0LmyKVoU+flb8Xg8PPzwvaEWpdljNpu59NLLefjhx4nx2EnInYPJdjhwDeoeorf/TMzO3xg+4lSmTJlGhw6h2EQRfHr0EFUewUNOGBpS7+Cm0FSpm7e5V/iF/Py8qtcOhyMSRgXNQq8HDhzMU08+S3JsFPG5c7Ac2ur3NjSnjbhN3xG1fyPnnnsBd/393ohfEG4IJpOpyhnOHykjQ4XPawRCiCSqzYFKKfcDMwIhlCI8WbJkEYsWLWzwfTk562sc33ffnfTp069BdYwePZZRo0Y3uO36KCsrxeOxWUaOHNkamp9eZ2Z25blnn2fGjGeRcgmOkj1G4Do/+AeYi/cQl78Ei+7k5r/+ndNPH+MHiSOPuLh4jhwpiugFcZ+0QQjxDoZXZRHefATAYCnlNwGUTaEIKE8++Shr164hLi6mB/AdzVSv09LSmTx5Kh9++B5ffvkpVtshyrqdjh7TyJ0tuk7UnrVE7/6ddm3bc++9Dx0TyK8lUWkI4uMbt2U3HPC1WyCklMqDqYUzatToRvXKL7303GPOhYP/xfbt2/jkk6/IyEjcSM1wEc0Os9nMlVdeQ69efXjxpemYNn6NrcvJRryiBqA57cbW0CM7GTHiVG699W/ExoZfisxQEKnrA+D7GsGvQojInQBTKGqhT5++bN9eEGoxgsqQIScyfdpLdMvMJHbrYqK3/wIe3/L4mkr3k7Dxa6LL9nLTTX/lrrvuU0aAiIz7dwy+jgiOAL8JIUr5M1Vl8LMnKBR+JD4+gRtvvIbo6KgBhYWFe2ghep2R0Zonn3iGd9+dzfz532C2H8bWfTRYjr/Iazm4hdhty2mVns799z1N166NcFZThC2+GoLRQFr1uCoKRaTz++8rmTfvf7Rrl7qWZj41dDRWq5Xrr7+Z7t2zePXVlzHJeZT1HI9uPTYjmXXvemJ2/Eqfvv259x8PRpzXrKJ+fJ0a2gRE7pK4QlELnTp1prAwgPvrI4BRo0YzadJkol024jctgKOS1Vv35xKz41eGDTuZSREYOiEYREVF/nZZXw3ByUCBEGKP9293IIVSKILBunVruOSScxk2bNiAlqzX/foN4IEHJmEqLyI2f1mVX7WpdD8x238mO3sId911H1Zr8EJVRBJXX309WVk9ychoHWpRGo2m15dhIQxwOt16UZGt/oJBREUf9Z3vvpvHG2+8WnV88823M3bsmSGUqCYZGYmrCNHUUDjp9ldffcZ7772NOyYZ3RKDxV1OWlwUM6a/FNFbI1syvuq2r34Ew4DrqJazWEo5vkkSKloM48efXcMQhIsRWL9+HfPmfU1FhS3zu+++m00L1+sJEy5gxc8/sXXrFjweNzhK+es/nlBGoAXg69TQq8APQDKwDTgYKIEUzZMbb7wNMEYD4cL06VMZNGgIZWVlZnzQayGESQjxmhBihRDiByFE1lHXbxJCrBRC/CyEmBBI2QOB2WzmsksvB92DyVGKEL0ZOHBQqMVSBAFfDcFBKeUHQLGU8nGgY+BEUjRHxo8/m08++SZsRgMAyckpjB17JnFxcW4f9boqeT3wIEbyegCEEG2BOzHW08YDU4UQEbeKOGBAdtXrYcNGhFASRTDxdfuoRwjRF4jzOpal1VVYCGECXgEGAhXAjVLKLdWu3wTcAriAp6SUcxojvELRFEwmE3l5W6moqDD5otcclbxeCFF97vUkYLmUsgKoEEJsAQYAvwVC9kBhsfz5k9C9e48QSqIIJr6OCO4B+gIvAe8Db9ZTvtn3nBSRzx133E1+fh5XX331PnzT61qT1x/nWgnGVGrEkpKSGmoRFEHC1xHB9VLKf3hfD/GhfET0nBobTRP+TF5fuXuooQQqmqbCd+bO/Zq//e1uMjISy6SUvuwaqit5/dHXEjGCNEYsymeg5eDriKCPECKlAfU2+55TSkoaKSn1zSQowpmCgnxKSkoacsty4Gyo2klXPc/xr8CpQogYIUQy0BtYf2wV4c+FF16KppmIizvWy1jRPPF1RNAHOCSEOIDhblJfTJaI6Dk1NpqmonlQUJDHOeecQVJS0sCioiJfYg0dN3m9lPJrIcRLwFKMDtbDUsrygD9EALj88qu44IJLIjqapqJhBMShTAhxMXCulPJab8/pMSnlWd5rbYGFwIlANPALkF3XlyacnG4UzQ/lUKZorviq2z4ZAiHEoqNOOYEdGDt+CmopX7lraADenhPGkLqy53QTcDNGz2mKlPKzekQ4gLHPW6HwG5dddllPAKvVGrNy5cpl1KPXAULptiKQdAEy6ivkqyGYjTE/uhQYDpwLzAOukFKe0TQ5FYrQoPRaoTDwdRKws5TyDWnwNpAkpXyTBuQ8VijCEKXXCgW+K3yUEGI8sAIYAViFEN0Ata1AEckovVYo8H1EcC2GJ/AvwPXev2EYjmYKRaRyLUqvFYq61wiEEBYppUsIEVVZHm+0ciml47g3KhRhjNJrhaIm9Y0I3vH+lxhZyg56X+cGUiiFIsAovVYoqlGnIZBSXuF9eTvgAbZgBIq7IcByKRQBQ+m1QlETXxeLHwOGSikPeB3CvsSYS22R1BddVVE7QoihwLNSytNCLYsXpdfVUHrdOMJQrxuMr4vFJVLKAwBSyr1AWeBEigiOG11VUTtCiPuBN4CYUMtSDaXXNVF63UDCVK8bTJ0jAiHElMpyQog5wDKM6KEVgRYszKkruqqidrYCFwHvhloQpdfHRel1wwkbvW4K9U0NyaP+A3wVIFkiiVqjq1YLrKc4CinlZ0KIzFDL4UXpde0ovW4gYabXjaZOQyCl/E+wBIkw6oquqghzlF4fF6XXLRQVZ7Zx1BWXXqGIVJRet1BUTJXGcUxc+hDLo1D4A6XXLZSA5CNQKBQKReSgpoYUCoWihaMMgUKhULRwlCFQKBSKFo4yBAqFQtHCUYZAoVAoWjhq+2gQEUKciZEecVYj7o0BcqWUmQ28723gQ+9hg9oWQqQBZ0op329Im4qWh9LtyEYZgiAipfw2wtoeAJwHqC+Lok6Ubkc2yhAEESHEtUAvoA+QjJEb92Ep5YLjlE8A/gukYsTMrzz/A3CrlDJXCHEr0BZ4G/gE2AN0BOZLKR8+um0p5YNCiEkYkSYtwKtSyteFEFOBE4B0YI2U8jrgYWCgEOJmYD4wC4gF7MDNUsodfnhbFM0ApduRjVojCD7dgVbAucDl1G2MbwXWSylHAq/7UHcmRh7eE4HRQojBRxcQQgwCzgKGYkTc7CmESAYKpZRjMb4ww4QQHYCngUXeIfc04CVvzPVpwDM+yKNoWSjdjlDUiCD4bAXmAB8AVuClOsr2BOYCSCl/EUI4aymjVXu9Rkp5GEAI8QsgaikvgF+llG7ADfxDCGEFWgshPgBKgQSvbNXpD0wUQjzgbbM2WRQtG6XbEYoaEQSfHkCilPIc4Brg5TrK5gDDoaq3U6nA5UA77+vqPaPeQog4IYQZo1eUU0uducBgIYRJCGEVQizECDTWSUp5OTARY4isYaRxNFW77wFvr+kWjKG6QlEdpdsRihoRBJ/NwGlCiMswFPHROsq+BrwjhFiGoayViVNeAl4RQmwHdlUr78BQ4jbAp1LKNULU7DhJKVcLIb7FiDRpAl4FfgEmCSF+BHQgD2iP0cPrL4S4C7gXeNW7wyMW+Hsjn1/RfFG6HaGooHPNBG9yjA+llC02566ieaJ0O/CoEUEYIIR4BWO3xdGcJaW0B1sehcJfKN2ODNSIQKFQKFo4arFYoVAoWjjKECgUCkULRxkChUKhaOEoQ6BQKBQtHGUIFAqFooXz/wHxTNzo3hfrRAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# учитываем каждую букву больше одного раза\n",
    "v_num = m_q1[:, ix_bigrams].minimum(m_q2[:, ix_bigrams]).sum(axis=1)\n",
    "v_den = m_q1[:, ix_bigrams].sum(axis=1) + m_q2[:, ix_bigrams].sum(axis=1)\n",
    "v_score = np.array(v_num.flatten()).astype(np.float32)[0, :]/np.array(v_den.flatten())[0, :]\n",
    "\n",
    "df['bigram_all_jaccard'] = v_score\n",
    "plot_real_feature('bigram_all_jaccard')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# учитываем каждую букву больше одного раза\n",
    "# нормируем максимальным значением, а не суммой\n",
    "v_num = m_q1[:, ix_bigrams].minimum(m_q2[:, ix_bigrams]).sum(axis=1)\n",
    "v_den = m_q1[:, ix_bigrams].maximum(m_q2[:, ix_bigrams]).sum(axis=1)\n",
    "v_score = np.array(v_num.flatten()).astype(np.float32)[0, :]/np.array(v_den.flatten())[0, :]\n",
    "\n",
    "df['bigram_all_jaccard_max'] = v_score\n",
    "plot_real_feature('bigram_all_jaccard_max')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Триграмы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "m_q1 = m_q1[:, ix_trigrams]\n",
    "m_q2 = m_q2[:, ix_trigrams]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "v_num = (m_q1 > 0).minimum((m_q2 > 0)).sum(axis=1)\n",
    "v_den = (m_q1 > 0).maximum((m_q2 > 0)).sum(axis=1)\n",
    "v_den[np.where(v_den == 0)] = 1\n",
    "v_score = np.array(v_num.flatten()).astype(np.float32)[0, :]/np.array(v_den.flatten())[0, :]\n",
    "\n",
    "df['trigram_jaccard'] = v_score\n",
    "plot_real_feature('trigram_jaccard')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# учитываем каждую букву больше одного раза\n",
    "v_num = m_q1.minimum(m_q2).sum(axis=1)\n",
    "v_den = m_q1.sum(axis=1) + m_q2.sum(axis=1)\n",
    "v_den[np.where(v_den == 0)] = 1\n",
    "v_score = np.array(v_num.flatten()).astype(np.float32)[0, :]/np.array(v_den.flatten())[0, :]\n",
    "\n",
    "df['trigram_all_jaccard'] = v_score\n",
    "plot_real_feature('trigram_all_jaccard')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# учитываем каждую букву больше одного раза\n",
    "# нормируем максимальным значением, а не суммой\n",
    "v_num = m_q1.minimum(m_q2).sum(axis=1)\n",
    "v_den = m_q1.maximum(m_q2).sum(axis=1)\n",
    "v_den[np.where(v_den == 0)] = 1\n",
    "v_score = np.array(v_num.flatten()).astype(np.float32)[0, :]/np.array(v_den.flatten())[0, :]\n",
    "\n",
    "df['trigram_all_jaccard_max'] = v_score\n",
    "plot_real_feature('trigram_all_jaccard_max')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tf-idf и попарные метрики на триграмах"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tft = TfidfTransformer(\n",
    "    norm='l2', \n",
    "    use_idf=True, \n",
    "    smooth_idf=True, \n",
    "    sublinear_tf=False)\n",
    "\n",
    "tft = tft.fit(sparse.vstack((m_q1, m_q2)))\n",
    "m_q1_tf = tft.transform(m_q1)\n",
    "m_q2_tf = tft.transform(m_q2)\n",
    "\n",
    "v_num = np.array(m_q1_tf.multiply(m_q2_tf).sum(axis=1))[:, 0]\n",
    "v_den = np.array(np.sqrt(m_q1_tf.multiply(m_q1_tf).sum(axis=1)))[:, 0] * \\\n",
    "        np.array(np.sqrt(m_q2_tf.multiply(m_q2_tf).sum(axis=1)))[:, 0]\n",
    "v_num[np.where(v_den == 0)] = 1\n",
    "v_den[np.where(v_den == 0)] = 1\n",
    "\n",
    "v_score = 1 - v_num/v_den\n",
    "\n",
    "df['trigram_tfidf_cosine'] = v_score\n",
    "plot_real_feature('trigram_tfidf_cosine')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tft = TfidfTransformer(\n",
    "    norm='l2', \n",
    "    use_idf=True, \n",
    "    smooth_idf=True, \n",
    "    sublinear_tf=False)\n",
    "\n",
    "tft = tft.fit(sparse.vstack((m_q1, m_q2)))\n",
    "m_q1_tf = tft.transform(m_q1)\n",
    "m_q2_tf = tft.transform(m_q2)\n",
    "\n",
    "v_score = (m_q1_tf - m_q2_tf)\n",
    "v_score = np.sqrt(np.array(v_score.multiply(v_score).sum(axis=1))[:, 0])\n",
    "\n",
    "df['trigram_tfidf_l2_euclidean'] = v_score\n",
    "plot_real_feature('trigram_tfidf_l2_euclidean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tft = TfidfTransformer(\n",
    "    norm='l1', \n",
    "    use_idf=True, \n",
    "    smooth_idf=True, \n",
    "    sublinear_tf=False)\n",
    "\n",
    "tft = tft.fit(sparse.vstack((m_q1, m_q2)))\n",
    "m_q1_tf = tft.transform(m_q1)\n",
    "m_q2_tf = tft.transform(m_q2)\n",
    "\n",
    "v_score = (m_q1_tf - m_q2_tf)\n",
    "v_score = np.sqrt(np.array(v_score.multiply(v_score).sum(axis=1))[:, 0])\n",
    "\n",
    "df['trigram_tfidf_l1_euclidean'] = v_score\n",
    "plot_real_feature('trigram_tfidf_l1_euclidean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tft = TfidfTransformer(\n",
    "    norm='l2', \n",
    "    use_idf=False, \n",
    "    smooth_idf=True, \n",
    "    sublinear_tf=False)\n",
    "\n",
    "tft = tft.fit(sparse.vstack((m_q1, m_q2)))\n",
    "m_q1_tf = tft.transform(m_q1)\n",
    "m_q2_tf = tft.transform(m_q2)\n",
    "\n",
    "v_score = (m_q1_tf - m_q2_tf)\n",
    "v_score = np.sqrt(np.array(v_score.multiply(v_score).sum(axis=1))[:, 0])\n",
    "\n",
    "df['trigram_tf_l2_euclidean'] = v_score\n",
    "plot_real_feature('trigram_tf_l2_euclidean')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Проверяем с новыми признаками"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictors = df.columns[7:].tolist()\n",
    "print (predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_corr(predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.pairplot(df.loc[ix_train][[\n",
    "    'abs_diff_len1_len2', \n",
    "    'trigram_jaccard',\n",
    "    'trigram_tfidf_cosine', \n",
    "    'trigram_tfidf_l2_euclidean', \n",
    "    'trigram_tfidf_l1_euclidean',\n",
    "    'is_duplicate']], hue=\"is_duplicate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.isfile('./../data/tmp/2_model.pkl'):\n",
    "    model = check_model(predictors)\n",
    "    print (model.best_score_)\n",
    "    print (model.best_params_)\n",
    "    with open('./../data/tmp/2_model.pkl', 'wb') as f:\n",
    "        pickle.dump(model, f)\n",
    "else:\n",
    "    with open('./../data/tmp/2_model.pkl', 'rb') as f:\n",
    "        model = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "Fitting 5 folds for each of 90 candidates, totalling 450 fits\n",
    "[Parallel(n_jobs=-1)]: Done  18 tasks      | elapsed:   34.0s\n",
    "[Parallel(n_jobs=-1)]: Done 168 tasks      | elapsed:  3.2min\n",
    "[Parallel(n_jobs=-1)]: Done 418 tasks      | elapsed:  7.9min\n",
    "[Parallel(n_jobs=-1)]: Done 450 out of 450 | elapsed:  8.4min finished\n",
    "0.716752825942\n",
    "{'en__l1_ratio': 1, 'en__alpha': 1e-05}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred = model.predict_proba(df.loc[ix_test][predictors])[:, 1]\n",
    "y_test_pred_fixed = link_function(y_test_pred)\n",
    "\n",
    "if not os.path.isfile('./../data/tmp/2_pred_fixed.csv'):\n",
    "    pd.DataFrame.from_records(\n",
    "        zip(df.loc[ix_test]['test_id'].values, \n",
    "            y_test_pred_fixed), \n",
    "        columns=['test_id', 'is_duplicate']).to_csv('./../data/tmp/2_pred_fixed.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = plt.subplot2grid((1, 2), (0, 0))\n",
    "ax2 = plt.subplot2grid((1, 2), (0, 1))\n",
    "sns.distplot(y_test_pred, ax=ax1)\n",
    "ax1.set_title('Model prediction')\n",
    "sns.distplot(y_test_pred_fixed, ax=ax2)\n",
    "ax2.set_title('Model prediction fixed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "display(\n",
    "    eli5.sklearn.explain_linear_classifier_weights(\n",
    "        model.best_estimator_.steps[1][1],\n",
    "        feature_names=predictors))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "- Старое значение: **0.44127**\n",
    "- Новый результат: **0.39816**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Out-of-fold prediction на триграмах\n",
    "\n",
    "<img src=\"./../images/buben3.jpg\" />\n",
    "\n",
    "#### Используем tf-idf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_model(predictors, data=None, do_scaling=True):\n",
    "    classifier = lambda: SGDClassifier(\n",
    "        loss='log', \n",
    "        penalty='elasticnet', \n",
    "        fit_intercept=True, \n",
    "        n_iter=100, \n",
    "        shuffle=True, \n",
    "        n_jobs=-1,\n",
    "        class_weight=None)\n",
    "\n",
    "    steps = []\n",
    "    if do_scaling:\n",
    "        steps.append(('ss', StandardScaler()))\n",
    "    steps.append(('en', classifier()))\n",
    "    \n",
    "    model = Pipeline(steps=steps)\n",
    "\n",
    "    parameters = {\n",
    "        'en__alpha': [0.00001, 0.0001, 0.001, 0.01, 0.02, 0.1, 0.5, 0.9, 1],\n",
    "        'en__l1_ratio': [0, 0.0001, 0.001, 0.01, 0.1, 0.3, 0.5, 0.75, 0.9, 1]\n",
    "    }\n",
    "\n",
    "    folder = StratifiedKFold(n_splits=5, shuffle=True)\n",
    "    \n",
    "    grid_search = GridSearchCV(\n",
    "        model, \n",
    "        parameters, \n",
    "        cv=folder, \n",
    "        n_jobs=-1, \n",
    "        verbose=1)\n",
    "    if data is None:\n",
    "        grid_search = grid_search.fit(df.loc[ix_train][predictors], \n",
    "                                      df.loc[ix_train]['is_duplicate'])\n",
    "    else:\n",
    "        grid_search = grid_search.fit(data['X'], \n",
    "                                      data['y'])\n",
    "    \n",
    "    return grid_search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.isfile('./../data/tmp/3_model.pkl'):\n",
    "    model = check_model(None, data={\n",
    "        'X': sparse.csc_matrix(sparse.hstack((m_q1_tf, m_q2_tf)))[ix_train, :],\n",
    "        'y': df.loc[ix_train]['is_duplicate']\n",
    "    }, do_scaling=False)\n",
    "    print (model.best_score_)\n",
    "    print (model.best_params_)\n",
    "    with open('./../data/tmp/3_model.pkl', 'wb') as f:\n",
    "        pickle.dump(model, f)\n",
    "else:\n",
    "    with open('./../data/tmp/3_model.pkl', 'rb') as f:\n",
    "        model = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "Fitting 5 folds for each of 90 candidates, totalling 450 fits\n",
    "[Parallel(n_jobs=-1)]: Done  18 tasks      | elapsed:  3.3min\n",
    "[Parallel(n_jobs=-1)]: Done 168 tasks      | elapsed: 18.8min\n",
    "[Parallel(n_jobs=-1)]: Done 418 tasks      | elapsed: 43.8min\n",
    "[Parallel(n_jobs=-1)]: Done 450 out of 450 | elapsed: 46.3min finished\n",
    "0.741522174677\n",
    "{'en__l1_ratio': 0.0001, 'en__alpha': 1e-05}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred = model.predict_proba(\n",
    "    sparse.csc_matrix(sparse.hstack((m_q1_tf, m_q2_tf)))[ix_test, :])[:, 1]\n",
    "y_test_pred_fixed = link_function(y_test_pred)\n",
    "\n",
    "if not os.path.isfile('./../data/tmp/3_pred_fixed.csv'):\n",
    "    pd.DataFrame.from_records(\n",
    "        zip(df.loc[ix_test]['test_id'].values, \n",
    "            y_test_pred_fixed), \n",
    "        columns=['test_id', 'is_duplicate']).to_csv('./../data/tmp/3_pred_fixed.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = plt.subplot2grid((1, 2), (0, 0))\n",
    "ax2 = plt.subplot2grid((1, 2), (0, 1))\n",
    "sns.distplot(y_test_pred, ax=ax1)\n",
    "ax1.set_title('Model prediction')\n",
    "sns.distplot(y_test_pred_fixed, ax=ax2)\n",
    "ax2.set_title('Model prediction fixed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Старое значение: **0.39816**\n",
    "- Новый результат: **0.36629**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "tmp = set(ix_trigrams.tolist())\n",
    "tmp = [k for (k, v) in sorted(cv_char.vocabulary_.items(), key=lambda t: t[1]) if v in tmp]\n",
    "tmp = tmp + tmp\n",
    "display(\n",
    "    eli5.sklearn.explain_linear_classifier_weights(\n",
    "        model.best_estimator_.steps[0][1],\n",
    "        feature_names=tmp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[:1000][df['question1'][:1000].apply(lambda s: 'o v' in s)]['question1'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[:1000][df['question1'][:1000].apply(lambda s: 'o v' in s)]['question2'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[:1000][df['question1'][:1000].apply(lambda s: '.co' in s)]['question1'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[:1000][df['question1'][:1000].apply(lambda s: '.co' in s)]['question2'].tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<img src=\"./../images/oof.png\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {\n",
    "    'X_train': sparse.csc_matrix(sparse.hstack((m_q1_tf, m_q2_tf)))[ix_train, :],\n",
    "    'y_train': df.loc[ix_train]['is_duplicate'],\n",
    "    'X_test': sparse.csc_matrix(sparse.hstack((m_q1_tf, m_q2_tf)))[ix_test, :],\n",
    "    'y_train_pred': np.zeros(ix_train.shape[0]),\n",
    "    'y_test_pred': []\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.isfile('./../data/tmp/4_model_pred.pkl'):\n",
    "    n_splits = 10\n",
    "    folder = StratifiedKFold(n_splits=n_splits, shuffle=True)\n",
    "    for ix_first, ix_second in tqdm_notebook(folder.split(np.zeros(data['y_train'].shape[0]), data['y_train']), \n",
    "                                             total=n_splits):\n",
    "        # {'en__l1_ratio': 0.0001, 'en__alpha': 1e-05}\n",
    "        model = SGDClassifier(\n",
    "            loss='log', \n",
    "            penalty='elasticnet', \n",
    "            fit_intercept=True, \n",
    "            n_iter=100, \n",
    "            shuffle=True, \n",
    "            n_jobs=-1,\n",
    "            l1_ratio=0.0001,\n",
    "            alpha=1e-05,\n",
    "            class_weight=None)\n",
    "        model = model.fit(data['X_train'][ix_first, :], data['y_train'][ix_first])\n",
    "        data['y_train_pred'][ix_second] = model.predict_proba(data['X_train'][ix_second, :])[:, 1]\n",
    "        data['y_test_pred'].append(model.predict_proba(data['X_test'])[:, 1])\n",
    "        \n",
    "    data['y_test_pred'] = np.array(data['y_test_pred']).T.mean(axis=1)\n",
    "    with open('./../data/tmp/4_model_pred.pkl', 'wb') as f:\n",
    "        pickle.dump({\n",
    "            'model': model,\n",
    "            'y_train_pred': data['y_train_pred'],\n",
    "            'y_test_pred': data['y_test_pred']\n",
    "        }, f)\n",
    "else:\n",
    "    with open('./../data/tmp/4_model_pred.pkl', 'rb') as f:\n",
    "        tmp = pickle.load(f)\n",
    "        model = tmp['model']\n",
    "        data['y_train_pred'] = tmp['y_train_pred']\n",
    "        data['y_test_pred'] = tmp['y_test_pred']\n",
    "        del(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = plt.subplot2grid((1, 2), (0, 0))\n",
    "ax2 = plt.subplot2grid((1, 2), (0, 1))\n",
    "\n",
    "sns.distplot(data['y_train_pred'], label='train', ax=ax1)\n",
    "sns.distplot(data['y_test_pred'], label='test', ax=ax1)\n",
    "ax1.axvline(np.mean(data['y_train_pred']), color='b', alpha=1, linestyle='--', label='mean(train)')\n",
    "ax1.axvline(np.mean(data['y_test_pred']), color='g', alpha=1, linestyle='--', label='mean(test)')\n",
    "ax1.legend(loc='upper right', prop={'size': 18})\n",
    "ax1.set_title('Train/test OOF predictions')\n",
    "\n",
    "sns.distplot(data['y_train_pred'][:ix_train.shape[0]//2], label='train first part', ax=ax2)\n",
    "sns.distplot(data['y_train_pred'][ix_train.shape[0]//2:], label='train second part', ax=ax2)\n",
    "ax2.legend(loc='upper right', prop={'size': 18})\n",
    "ax2.set_title('Train/train OOF predictions')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Необходимо пофиксить распределение, хотя бы среднее значение, воспользуемся уже знакомым преобразованием:\n",
    "\n",
    "$$\\large g\\left(x\\right) = \\frac{w_1 x}{w_1 x + w_0 \\left(1 - x\\right)} = \\frac{w x}{w x + (1 - w) \\left(1 - x\\right)}$$\n",
    "\n",
    "- $w \\in \\left[0, 1\\right]$\n",
    "\n",
    "Условие:\n",
    "\n",
    "$$\\large\\begin{array}{rcl}\n",
    "\\mathbb{E}\\left[p\\right] = \\mathbb{E}\\left[g\\left(q\\right)\\right]\n",
    "\\end{array}$$\n",
    "- $p$ - распределение на тренировочном наборе\n",
    "- $q$ - распределение на тестовом наборе\n",
    "\n",
    "Можно составить уравнение:\n",
    "\n",
    "$$\\large\\begin{array}{rcl}\n",
    "\\frac{1}{n} \\sum_{i=1}^n p_i &=& \\frac{1}{m} \\sum_{i=1}^m g\\left(q_i\\right) \\Rightarrow \\\\\n",
    "\\mu_p &=& \\frac{1}{m} \\sum_{i=1}^n \\frac{wq_i}{wq_i + \\left(1 - w\\right)\\left(1 - q_i\\right)} \n",
    "\\end{array}$$\n",
    "\n",
    "Получается следующая задача оптимизации:\n",
    "$$\\large \\hat{w} = \\arg\\min_w \\left(\\mu_p - \\frac{1}{m} \\sum_{i=1}^n \\frac{wq_i}{wq_i + \\left(1 - w\\right)\\left(1 - q_i\\right)}\\right)^2 $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.36944607019869374\n",
      "2187887344478.122\n",
      "      fun: 8.428638216930092e-05\n",
      " hess_inv: <1x1 LbfgsInvHessProduct with dtype=float64>\n",
      "      jac: array([0.00064127])\n",
      "  message: b'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH'\n",
      "     nfev: 56\n",
      "      nit: 11\n",
      "   status: 0\n",
      "  success: True\n",
      "        x: array([0.55376469])\n"
     ]
    }
   ],
   "source": [
    "mp = np.mean(data['y_train_pred'])\n",
    "print (mp)\n",
    "\n",
    "def func(w):\n",
    "    return (mp*data['y_test_pred'].shape[0] - \n",
    "            np.sum(w[0]*data['y_test_pred']/(w[0]*data['y_test_pred'] + \n",
    "                                             (1 - w[0]) * (1 - data['y_test_pred']))))**2\n",
    "\n",
    "print (func(np.array([1])))\n",
    "\n",
    "res = minimize(func, np.array([1]), method='L-BFGS-B', bounds=[(0, 1)])\n",
    "\n",
    "print (res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "w = res['x'][0]\n",
    "\n",
    "def fix_function(x):\n",
    "    return w*x/(w*x + (1 - w)*(1 - x))\n",
    "\n",
    "support = np.linspace(0, 1, 1000)\n",
    "values = fix_function(support)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(support, values)\n",
    "ax.set_title('Fix transformation', fontsize=20)\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('f(x)')\n",
    "plt.show()\n",
    "\n",
    "data['y_test_pred_fixed'] = fix_function(data['y_test_pred'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = plt.subplot2grid((1, 2), (0, 0))\n",
    "ax2 = plt.subplot2grid((1, 2), (0, 1))\n",
    "\n",
    "sns.distplot(data['y_train_pred'], label='train', ax=ax1)\n",
    "sns.distplot(data['y_test_pred_fixed'], label='test', ax=ax1)\n",
    "ax1.axvline(np.mean(data['y_train_pred']), color='b', alpha=1, linestyle='--', label='mean(train)')\n",
    "ax1.axvline(np.mean(data['y_test_pred_fixed']), color='g', alpha=1, linestyle='--', label='mean(test)')\n",
    "ax1.legend(loc='upper right', prop={'size': 18})\n",
    "ax1.set_title('Train/test OOF predictions')\n",
    "\n",
    "sns.distplot(data['y_train_pred'][:ix_train.shape[0]//2], label='train first part', ax=ax2)\n",
    "sns.distplot(data['y_train_pred'][ix_train.shape[0]//2:], label='train second part', ax=ax2)\n",
    "ax2.legend(loc='upper right', prop={'size': 18})\n",
    "ax2.set_title('Train/train OOF predictions')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['m_q1_q2_tf_oof'] = np.zeros(df.shape[0])\n",
    "df.loc[ix_train, 'm_q1_q2_tf_oof'] = data['y_train_pred']\n",
    "df.loc[ix_test, 'm_q1_q2_tf_oof'] = data['y_test_pred_fixed']\n",
    "del(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictors = df.columns[7:].tolist()\n",
    "print (predictors)\n",
    "\n",
    "if not os.path.isfile('./../data/tmp/5_model.pkl'):\n",
    "    model = check_model(predictors)\n",
    "    print (model.best_score_)\n",
    "    print (model.best_params_)\n",
    "    with open('./../data/tmp/5_model.pkl', 'wb') as f:\n",
    "        pickle.dump(model, f)\n",
    "else:\n",
    "    with open('./../data/tmp/5_model.pkl', 'rb') as f:\n",
    "        model = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "Fitting 5 folds for each of 90 candidates, totalling 450 fits\n",
    "[Parallel(n_jobs=-1)]: Done  18 tasks      | elapsed:   35.5s\n",
    "[Parallel(n_jobs=-1)]: Done 168 tasks      | elapsed:  3.5min\n",
    "[Parallel(n_jobs=-1)]: Done 418 tasks      | elapsed:  8.6min\n",
    "[Parallel(n_jobs=-1)]: Done 450 out of 450 | elapsed:  9.1min finished\n",
    "0.784444334512\n",
    "{'en__l1_ratio': 0.5, 'en__alpha': 1e-05}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred = model.predict_proba(df.loc[ix_test][predictors])[:, 1]\n",
    "y_test_pred_fixed = link_function(y_test_pred)\n",
    "\n",
    "if not os.path.isfile('./../data/tmp/5_pred_fixed.csv'):\n",
    "    pd.DataFrame.from_records(\n",
    "        zip(df.loc[ix_test]['test_id'].values, \n",
    "            y_test_pred_fixed), \n",
    "        columns=['test_id', 'is_duplicate']).to_csv('./../data/tmp/5_pred_fixed.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = plt.subplot2grid((1, 2), (0, 0))\n",
    "ax2 = plt.subplot2grid((1, 2), (0, 1))\n",
    "sns.distplot(y_test_pred, ax=ax1)\n",
    "ax1.set_title('Model prediction')\n",
    "sns.distplot(y_test_pred_fixed, ax=ax2)\n",
    "ax2.set_title('Model prediction fixed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "display(\n",
    "    eli5.sklearn.explain_linear_classifier_weights(\n",
    "        model.best_estimator_.steps[1][1],\n",
    "        feature_names=predictors))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Старое значение: **0.39816** и **0.36629**\n",
    "- Новый результат: **0.3316**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_real_feature('m_q1_q2_tf_oof')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "del(unigrams, bigrams, trigrams)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Факторизация горизонтально сконкатенированной матрицы и опять OOF\n",
    "\n",
    "<img src=\"./../images/buben4.jpg\" />\n",
    "\n",
    "<br />\n",
    "\n",
    "<img src=\"./../images/mf1.png\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.isfile('./../data/tmp/1_svd.pkl'):\n",
    "    svd = TruncatedSVD(n_components=100)\n",
    "    m_svd = svd.fit_transform(sparse.csc_matrix(sparse.hstack((m_q1_tf, m_q2_tf))))\n",
    "    with open('./../data/tmp/1_svd.pkl', 'wb') as f:\n",
    "        pickle.dump(svd, f)\n",
    "    with open('./../data/tmp/1_m_svd.npz', 'wb') as f:\n",
    "        np.savez(f, m_svd)\n",
    "else:\n",
    "    with open('./../data/tmp/1_svd.pkl', 'rb') as f:\n",
    "        svd = pickle.load(f)\n",
    "    with open('./../data/tmp/1_m_svd.npz', 'rb') as f:\n",
    "        m_svd = np.load(f)['arr_0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x115aa7a90>]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.cumsum(svd.explained_variance_ratio_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = plt.subplot2grid((2, 2), (0, 0))\n",
    "ax2 = plt.subplot2grid((2, 2), (0, 1))\n",
    "ax3 = plt.subplot2grid((2, 2), (1, 0))\n",
    "ax4 = plt.subplot2grid((2, 2), (1, 1))\n",
    "\n",
    "ax1.scatter(m_svd[np.where(df['is_duplicate'] == 0), 0], \n",
    "            m_svd[np.where(df['is_duplicate'] == 0), 1],\n",
    "            color='g', alpha=0.1, label='not dub')\n",
    "ax1.scatter(m_svd[np.where(df['is_duplicate'] == 1), 0], \n",
    "            m_svd[np.where(df['is_duplicate'] == 1), 1],\n",
    "            color='r', alpha=0.1, label='dub')\n",
    "ax1.set_title('rSVD: 0 vs 1')\n",
    "ax1.legend(loc='upper right', prop={'size': 18})\n",
    "\n",
    "ax2.scatter(m_svd[np.where(df['is_duplicate'] == 0), 0], \n",
    "            m_svd[np.where(df['is_duplicate'] == 0), 2],\n",
    "            color='g', alpha=0.1)\n",
    "ax2.scatter(m_svd[np.where(df['is_duplicate'] == 1), 0], \n",
    "            m_svd[np.where(df['is_duplicate'] == 1), 2],\n",
    "            color='r', alpha=0.1)\n",
    "ax2.set_title('rSVD: 0 vs 2')\n",
    "\n",
    "ax3.scatter(m_svd[np.where(df['is_duplicate'] == 0), 1], \n",
    "            m_svd[np.where(df['is_duplicate'] == 0), 2],\n",
    "            color='g', alpha=0.1)\n",
    "ax3.scatter(m_svd[np.where(df['is_duplicate'] == 1), 1], \n",
    "            m_svd[np.where(df['is_duplicate'] == 1), 2],\n",
    "            color='r', alpha=0.1)\n",
    "ax3.set_title('rSVD: 1 vs 2')\n",
    "\n",
    "ax4.scatter(m_svd[np.where(df['is_duplicate'] == 0), 0], \n",
    "            m_svd[np.where(df['is_duplicate'] == 0), 3],\n",
    "            color='g', alpha=0.1)\n",
    "ax4.scatter(m_svd[np.where(df['is_duplicate'] == 1), 0], \n",
    "            m_svd[np.where(df['is_duplicate'] == 1), 3],\n",
    "            color='r', alpha=0.1)\n",
    "ax4.set_title('rSVD: 0 vs 3')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MLZAlmpnyvYV8Q36PezNSEUgkR84cfptJtjU81AkyDKTpCnuHIovfwnE7kjtowZ/UDD/Q+sWCusM97rVIH4FEIpH0cXpEjyAQCKh+v1RYEolE0hZMJkMJrRit3iMUgd+vUlHRbeZnkkgkkh5BfLy9VaP6e4QikEg6E1VVqe9wGvXtWmdGIunRSEUg6dV4/QG25lezo7CajMIacitdlNV5qHT68PoD+AIqvsBvZkeTQYfNZMBmNhBmMhBjM5Fkt5ASFcaoZDtj+kUSYZGfjaR3Id9oSa+jrM7DT5ll/LS3jJ+zyqn1aPMDxoWbSYkJY2hCBJFWEwa9jvwKJ3qdDr0eUMEbUIm3W3F6fTg9fiqcXn7JrqCkpggVbZ7scSmRnDw8gZnD4nDYjnSaKIkk9PSIqCGv169KH4HkUFQ6vSwVxXy5vYht+VWoQGy4mYkDY5g4IAYlyc6W/QevHDhlaDyrdxU3yau5/W5fgMToMLbkVPLjnlL2ldWh18HR/aM5TYnnxKFxRIV19LxpEsmRER9vX0+j2VGbQ/YIJD0Wrz/Aqr1lfLm9iB/3lOILqAyKtTF9cCxD4sNJiDCj0+nweP1EWI+skrYY9YxIjqSixs2lx6RQXONme0EN+8rreHTpLv7+3W4mDYjmtBEJnDgkDqvJ0EFXKZF0PlIRSLot/oBKYbWbarcPnz9AnddPWa2Xwmo363Iq2ZhTgdMbIMZm4pwxycwansDAuHBWiKJOly0+wsLxQyycOHwEu4pq+GFXCT/sKuGBxQKbeTczlXjmjE7iqGQ7Op10QEu6N1IRSLoVORVOFm0rZEXQ/OJpYfxIarSVEYl2BseFkx5rQ6/XkV1aS4rjSFeqbBsuv0pOWR2DY22kO/qzv9zJ5rwqluwo4ostBQyICWPOmGTOGpUoTUeSbotUBJJuwa7iGp5bsZefs8rRAWNSozh7TDKp0WHERlhAVbGY9MTYzMTYTIRbTF3S8m8LOp2ONIeNNIeNyYNj+WFXCd9sL+TZHzJ5eWUWM4fHc+WkNNJjwkItqkRyEFIRSEJKhdPLcz9ksmhbIREWI9MHOxjTL5LIBjb9sf2jWb2rmDo3lNd4AM2h253R6fVY9DrOHp3EpAEx/Lq/giU7ivhmeyGzRyYyb+pAEu1duQaNRNIyUhFIQsYPu0t4bOkuqlw+Lj0mlfPGp7Auq0PXsekWJNotzB6ZyPTBseRWuflyawHLdpZw47SBXDiuHwY5iE0SYqQikHQ5tb4AT363m0VbC0iPC+fRs0cxOD6c3j6dVITFyLwZKZwzJpnnl+/h6WV7+HpHEX89cwQDoqyhFk/Sh5EL00i6lAqnl9sXbmbR1gImDYjmgrHJZJfWsiyjCG+gl2sCwOkLkJFfxaxhcZw9OpHdxbVc9favrNhTGmrRJH0YqQgkXUZRtZur39vA9oJqzhqdyEnD4vusWUSn0zEqOZKrJvcnMdLCXZ9u45nle/D6A6EWTdIHkYpA0iWU1LiZ9+Fmyuq8PDFnNKOTI0MtUrfAYTMz/4IxzB3Xj/fW53Ld+5vIq3SFWixJH0MqAkmnU1bnYd6Hmymp8fDseaMZ1U8qgYZYTAaumz6I+2Yr7C2r43dvr2fRjiLcvd9SJukmSEUg6TTcKhTWefnDp9vIr3Lzt7NGkBYf0eudwm3F6QuwLKMIvy/A5cemEmE28vDiDJ76bpc0FUm6hJBEDSmKYgJeR1tn1QI8IoT4PBSySDqPWo+Pez7dyvb8auaMSaKk2s2yjKJuPwYglMTYzFw2MZVlO0v5ZFM+GQXVPHbmCFKj5SA0SecRqh7B74FSIcR04DTghRDJIelEFvyay7b8aqYPdjAi0R5qcXoMRr2ek4fH88Ds4eyvcPL7t3/l4835BHrATMGSnkmoFMGHwP3B/3WAL0RySDqJ9fsreGP1PkYkRjB1kCPU4vRIpg+N48WLxjE4PpzHl+7iqvc2sj6vSvoOJB1OSExDQogaAEVR7MBC4L5QyCHpHEprPfzlywz6RVmZPTJRzr7ZTurHHJw+IoH+0WF8v7OEee9v5Pxx/bh12iBsZjnVtaRjCJmzWFGU/sAy4G0hxHuhkkPSsfgDKg8szqDG7eO+2cOxGGU8wpGi0+kY0y+S648bwNh+kSzckMeFb65jSUYRPWFhKUn3JyRfqaIoicAS4B4hxOuhkEHSObyxJptfsiv4v5MGkx4XHmpxehU2s4HZIxN55vyjiA4z8ZcvM7jmf5vYml8VatEkPZxQNdfuBWKA+xVFWR78k2ERPZx12RX8e/U+Zo9I4OzRSaEWp9cyOiWK+XPHcOfMIeRWOrnqvY3ctziDwmp3qEWT9FBC5SO4Hbg9FGVLOoe8Gg/3frmDlOgwbpyRToU3IMcLdBJOX4DVu4qx6HVcOSmNn7PK+H5nMct3lXD5salcdmx/wuRSmZI2IA24kiMmoKo8vDiDapePU4fH83NmaZ+ZRC7UWIx6jh8Sx2u/H8/09Fj+vTqbC15fy+LthTLcVNJqpCKQHDHvrsvh1/0VzFLiiY+Qi62EguSoMP546jCePv8oom1mHvxKcPk7G1ibUxlq0SQ9AKkIJEfE9oJqXlyZxbTBsYxLkXMIhYr6aSqKq1ycNyaJM0clklvp5KYPNvF/n20jq7Qu1CJKujFSEUjaTa3Hx31f7iDWZuKOkwbL8QLdBJ1Ox1H9Irlh6kCunJzG2uwKLv7vOh5fuouSGulQljRFrlAmaTf//H4PuZUuXr5wzEFrDEu6B2aDnt9NTOP00Um8+8t+PttawJfbC7l0QiqXTUzDbpLtQImGfBMk7eKbHUV8ua2QqyalMT41OtTiSFrA6QuwYV85IxMjuHZKGgMdYbzxczaXv7WONfvKQy2epJsgFYGkTbhV2FFSy2Pf7mJksp0Ljkml3OOXoaI9AIfNzHlj+3Hh0f0IqHDLwi3cu2gHxdJc1OeRpiFJm6jx+Ljv8+34/CrHD45lxc5iADm1dA9icFw4V0wZyPvr9vP++hxWZpZyzph+nH90P5IjrVikq6fPIXsEkjbx2eZ8ssudzFLiiA6TfoGeih9IjbJyzeQ0BjhsvL8+h0tfX8v8ZbspqfWEWjxJFyN7BJJWs6+sjtdX7WNwnI0xcrnJXkGMzcy5Y5IpqfGwKquMjzfm8cWWAs4dk8z1UwZgt8oqoi8gewSSVuEPqDz8tcBs0DN7hJxaurcRF2Hm7NFJvH7ZMZwwLI4FG3I5/421fLy1AJccId7rkYpA0ireWZfDlvxqbjkhXbYSezGOCAvjU6K4YmJ/LEY9j3+zk0e+EfikMujVSEUgOSy7S2p5dVUWM4fFccLQuFCLI+kCkiKtXDGxP8cNcvDN9iL++Nk2XF5/qMWSdBJSEUgOidcf4KGvBBFmI/fMHCJNQn0IvU7H8UNiueX4dFZmlnHbx1txSmXQKwnlCmWTFEVZHqryJa3jlZ/2IYpquPfkocTYzKEWRxICzh6TzCNnDGdTbiV3fboNty8QapEkHUyoVij7I/AfwBqK8iWHx63CssxS3l67n9mjEhk7IEYOHOuj6HQ6jk2P5a6ZQ1mXXcFdn22jyOnFLd+FXkOoegR7gPNCVLakFRRUufjbYkGMzcTIxAiWZRTJNQb6KPUzmxp1cOqIBNZklXPnwi1Uu32hFk3SQYREEQghPgK8oShbcnj8AZW/L9lJrcfHOUclYTZIV5JE4+jUKGYOi0MU1fDE0p34ZcOgVyC/cEkTnl+xl3XZFZw6PIGkSGm9kxzMxAExHD8klmU7S3jwqww80mfQ45EB4ZKDWLStgHfX53DOmGRGJkaEWhxJN+W4QQ4Gx0fw+up9FFa7+efZo4i2ySlHeiqyRyA5wA+7S3hkyS4mpEVzw7SBoRZH0s25eEIqj505gu0F1Vzx3ga25FWFWiRJOwmZIhBCZAkhJoeqfMnB/LinlD99sYMRiRH88+yRGKVfQHIYdDodEwY5+Od5R+EPqFz7/kaeX7mXWhla1uOQX7uEJTtLuOfz7aTHhfPXs0bi1elkmKjksNRHExVUOLn0mBRGJNp5a81+bvpgI7mVzlCLJ2kDUhH0cd5bn8N9X2wn3m7h9JEJrN1bJsNEJW3GajJw9lFJnD06kazSOn731q98sbUAVZXvUU9AKoI+itcf4InvdvPM8kyOS3dw6TEphJkMoRZL0sMZlRzJq5cezaC4cP76zU7mLdzCjpJaOfismyMVQR+kqNrNDR9s5sONefzumFTumz0ck/QJSDqIKJuZM0cmcLISz5bcSq55+1de/jGTWo8cgNZdkeGjfYwf95TyyJKdOL1+HjtzBCcr8ZR75ERiko5Fp9MxIS2aofHhfL+rhHfX5rB4ayGXHZvKuWOSibDIqqc7IZ9GH6HW4+Op5Zl8saWAQbE2/nHuaAY4bHL+IEmnEhVm4twxyfSLCeOtn7N5bsVeXl+Tzflj+3Hx0f2Ii7CEWkQJUhH0elRV5dudJTyzfA8lNR4mD4xh+mAHmUU1ZBbVAHLheUnnMzwpkkfPGYUorObDX3N5e+1+3luXw2kjE7n82FQGOmyhFrFPIxVBL2ZbfhUvrMxiXXYFSkIE980eTkGFDOuTdD1OX4DVu4oBmDrIwYjECH7ZV8E3O4pYtLWA44fEcvmx/TlKroUdEqQi6IVsKqjm9dX7WJVZRpTVyM0z0jnzqCTQ6aQikHQLHDYzp41I4M+nKXy+KZ/PtuSzfHcpE9KiuWxSGsf0j8Yi10DqMqQi6CUEVJXVe8t5Z30O67IrsBj1zBgcy4S0aCxGPSt2FksTkKTbYTUb6R9t5fopA/g1p4Kfsyq4/cPNjEyyc+n4FE4cGofZKCPaOhupCHo4lU4vn28t4KNN+eRWukiIMHP91IFEmPVYjHJcgKRnYDbqmTzQwfjUaDblVbK9oIb7FmcQYTEwY3AsJw2NY/JABxapFDoFqQh6IAFVZUteFZ9vLeDrHcV4/AFG94vkyikDmJruQKfXs0IUhVpMiaTNmI16jk2L4f9OUVi/r5wfdpXw454yFm8vIsxkYHq6g5nD4jhukAOrHADZYUhF0EPw+gNsyq3ip71lfLezmPwqN1ajnlnD40m2W0iwWwj4A/y4q0SagCQ9HrdfpdrpZXxqFGP7RbKvvI4qt5/VmWUsEcVYjXqmDHIwPd3BtHSHXE/7CJGKoBviD6jkVbrILK0jo7CazXlVbMmvxun1Y9TrGJcaxWWT0jgu3YHFZJStf0mvxqDXkR4bzkkjEvGdEGBLXiU/7i5ldWYZy3aVADA4zsa4lCiGJ0SQHhfOgJgwIq1GdDrpcW4NIVEEiqLogZeAsYAbuFYIsTsUsoQKX0ClqNpNfpWLvEoX+VUu9le42FtaR1ZZHe7gqk96HQyJC2fW8HjMeh0DHLYDdtI1mWWy9S/pMzQMQR2dZGdUYgSF1W5UvZ6tuZV8tb2IjzblH0hvNuiJCzcRG24hLsKMw2YiwmIk0mIkwqr92i1G7FYjkVYjMTYTNpOhTyqPUPUI5gBWIcQURVEmA08B53R0ISU1bpbuLCEQUFHhwEyIqsqB7fpBtdo+9cAxGm2rwUSBA+dqidQG2/W5+QMqvoCK1x/A61fx+lVqPD6qXD6qXF6qXD6qXT4aDujVAXERZgbGhnPm6CQGxNoY4LAx0GEjzGzAryJb/hJJA3Q6HUmRVqYMjSc5wsysYXFUOH2U1Lgpd3pxRFgoq/VQVushs7SWDTk+atw+fIeYWddi1BMdZjqgNKxGPVaTAatRj8WoR6/ToaL56UD77gPBesTrD1Dn8eP0+qnzBHD5/Ae2/QEVi1FPmMlAmMmA1fRbvs3tMxv06HQQYTEye0RCp88FpgvFNLGKojwN/CKEeD+4nSuESDnEKcXAvi4RTiKRSHoPA4DDmg1C1SOIBCobbPsVRTEKIVqanlDaPyQSiaSTCFVQbhVgbyjHIZSARCKRSDqRUCmCn4DTAYI+gi0hkkMikUj6PKEyDX0CnKwoyio0P+lVIZJDIpFI+jwhcRZLJBKJpPsgJ+6QSCSSPo5UBBKJRNLH6XNTTBxuVLOiKNcBNwA+4BEhxKKQCNpJtOL6/wBcHNxcLIR4uOul7FxaM7I9mOZL4DMhxCtdL2Xn0Yp3YDbwIJr/bj1wsxCiV9nRZwGuAAAgAElEQVSQW3EP7gIuBQLAY0KIT0IiaBfRF3sEB0Y1A39CG9UMgKIoScBtwFTgVOBxRVF626Kqh7r+dOB3wHHAZOAURVHGhETKzqXFe9CAR4CYLpWq6zjUO2AH/gmcKYSYBGQBcaEQspM51D2IBm4HpgCnAPNDImEX0hcVwTTgawAhxM/AhAbHJgI/CSHcQohKYDfQ2yrCQ13/fuA0IYQ/2AI0Aa6uF7HTOdQ9QFGUC9Bagl93vWhdwqGu/zi0cO6nFEX5ESgUQhR3vYidzqHuQS3aTAbhwb9Al0vXxfRFRdDsqOYWjlUDUV0lWBfR4vULIbxCiBJFUXSKojwJbBBC7AyJlJ1Li/dAUZTRaCaBB0IhWBdxqG8gDjgRuAeYDdyhKMqwLpavKzjUPQCtUbQd+BV4risFCwV9UREcalRz42N2oKKrBOsiDjmqW1EUK/BuMM1NXSxbV3Goe3A5kAJ8D1wJ3KkoymldK16nc6jrLwXWCiEKhBA1wApgXFcL2AUc6h7MBpKBQUAaMEdRlIldLF+X0iPGEQQCAdXv7/5ySiQSSXfCZDKU0I0nnWsTfr9KRUVdqMWQSCSSHkV8vL1Vszb3RdOQRCKRSBrQI3oEEkmnoaroq/djKN+DoTILQ+VeDJVZGKtz0HlrwOcGcziqNYZATDqu5Cl4+03GH50easklkg5DKgJJn0JflYOpYC3G4q0Yi7dgKt6CzlN94LhqshGI7A+RyfidVah6EzqfC527GmPWMuwZ2rgib/xRuIedi0u5ADXMEarLkUg6hB7hLPZ6/ar0EUjai74qG+uOD7Du/RpDqQBANZgJOIZC3DC8fgjYEgiExaGaI0CnwzL4ONx7Vh2Uj1mZib88G8P+VRh3f4WheDuqwYxv6JlUj7kBf/yoUFyeRNIi8fH29TQaJ9Mcskcg6Z2oKqb9P2Db9G9M2StApyOQdDSuwWfgjxlCwJYAegOWwcfhbVTht4TO58RftAu/JR7PqMvR1xZiyl2NafdiHBkf40mehHPMVXjSTwO9/LQkPQf5tkpCzs6iGjbmVrI1v5oKlw+zQUdkmImjU6MZnxZFXLgFi66VmakqEZmfYl73IoaSDAK2eLzjr8M3/BwMEbF4dy7vMLkD4Ym4h81BPfVx2PE5pm0LiPrmRgLhiXiP+h3VI6+UZiNJj0AqAklIcAVUVuwp5X/rctiaVwWAw2bCEW6mrNZDjdvPF1sK0OtgyiAHl45P4di0aHS6FjSCqmLav4Lwn/+BqXgzgbA4XMr5eBOP1lrneVswDj6uU65FpzfgtibiGX8zhtIMzLmrsPz8NOa1L+IaOgfnmKul2UjSrZGKQNLlVNR5+cviDH7ZV47dYmSWEsew+AgirUaOG5bA6l3F+AMquRVOdhbXsiWvipszy0iLCeO0EQmcNjyB/jFhB/IzFqwn/Oe/Y85djd+eivv4B/GoZtAbuvbCdHr8cSNxxo3EED8Y3fbPsYqFhGV8oJmNxl6NJ3026DonatvprKWmphK/39sp+UtCj15vwGIJIzw8EqPR1GH5SkUg6VI25VZy76IdlNd5maXEMT41GoO+aSvfoNeR5rCR5rDxwBnDWbe3jC+2FvKvVfv416p9xNpMnBu1i7nuhSh1v1JtiOG7pJvYEjsbY6URQ1kmZj2Y9CpmvUqUKUCCzU1MnYGkMD+G1pqa2okhdhC+qXdQN+FqjCJoNvr6BvyOYdRMvDuoEDpOCK/XQ3V1OdHRcZhMlpZ7TpIei6qq+P1+XK5aysoKcTgSO0wZSEUg6TJW7S3j7s+2kWi38OyFY9hf2rpIMKvJyNSh8UwdGk95cR41v77PsPwv6F+eSQnRzA9cxDvek6nOtuLJqkJFR7MzSG8oBlIw6VTSwr2MivQwrq6GMT4TQ+1emtFH7UbnrSMQdEJ7gmYjY9FmLHmrifr6ejz9Z1B94pME7P06pLzq6goiIqIwm60dkp+k+6HT6TAajUREaPNg1tZWERUV2yF5S0Ug6VTcKtR5/azLLufBRTtIi7Hxj3NHEW4xtVoROD0e8td8wrCiRUwoW4lB9VMUMZKciY9THHMM4zOWMZ49AKQn2FFTjyVj3TJ8qg5vQI9X1REXE4U7/ij27hHk1BnZW2tiZUkYn+dVAMlEmfwcHe1igsPF5Dg3R3V0WLVOjy9xHPrpd8C2TzCveQ7H/07EPf3PeEfMxUX4EWXv83mwWKRjuq9gtYZTVlbQYflJRSDpVOq8ft5avY/3f83FYTNx5qhE1meVM2XoYefBwuivY0TBpwz/dQFjanPxWhyUDJ5Laf/TcEUOIsVhI9ZVS3Xj8/Q6LHoVCyoYtKnk08PdGOL0WLPyGGEH7KAmQvLRs1i4bB07asPZUhHB8uJwEBD3az7HRsVybKyLiQ43aTZfE/nag87vwW2KwXvMLVgzPsT6/X3oS3bimvq3I/JpBAJ+9F3tE5GEDIPBQCDg77D8pCKQdCqFVS4+3pSP3WrkkmNSsZkPX1mNdPjpn/0ZKdtfxeQuw5N4NMXj57EjrxrQw34BCLwJdkxp7Z8dWKeDfnYj02IqmBZTgapCsddEuSGeLf7+rN7n4asCraWeaPUxOauMY0zhTIp10S/syD5CNSwW59jrsOxehHnzW0RW5lB9ykvagLZ2X4/0C/QVOvpZS0Ug6TTqPH4eXLQDv6oyd1y/VikBc2UmR/94GxFlW6my9GNbymXEDRiBIWUi5H/XqfLqdJBg9jI9uYbfDXVQJXaQXWdkfbmVdWVWfsx28ZlTs8n2C/MyLtrNyEgvYywu0j16osxtXMhKb8A97BzU9BMw//QEUZ/OpfKM/6KGJ3T8xUkkh0AqAkmnoKoqf/tGkFVWx9xx/YgNNx/uBEbmf8SwVc8S0JsRCWdQaD8KdLouXzDX61eprPOwq1Ab3zDKCKMS4JnzTmXRt8vZXhvBjppwfiq2sTg/AkQJkEqy1Ydi96BEehmrczLepyPceHhfg2/kBbiihhP5zY3EfDyHyrPekZPaSboUqQgkncInm/P5dmcJ1xw3gITDKAGTr4YTdv6V9NJlVKfMIGvU7ZRnrusiSVuPXqcj1eom1ermlNhSAOIc0eRGHs2mjF3srDazq8bEiuIwAntKMepSOdbhYnZyHacm1WFrQSkY9SqkjMZ15qtYv76dmIVn4TrtWXypU47YidzbuOWW6ykoyGfhwi86Jf9HH32Ir75axMqV3e/960ykIpB0OLuLa3l6eSaTB8Ywd3wKPxxi7fOY2j2csuOPRDpzWT3odsJPuANfWXYXSntkRBr9xMTpsVhzmGgF4sET0GEaeiJLNu5mRbGNB7bG8vcdMZycVMu10V4GNsqjPtQ0ANSOuQbb5jewfnEd7pOfwDVk7hHLWB+51V2wmQytnzKkEVdccTVOp6tjBZJIRSDpWJxeP/cu2kG42cBDpynoD+HUmupdyYhN9xIwhrF76jNY4saSSAlum0pNF8rc0Zj1KuP7mdHtyeaUCNhVZ+OH8hgW50Xz2fuFTImN5/cDq5kW52oydkG1xVE3fh5hW97EsuQuvC43rtG/PyJ56rx+lmUUHVEeHcmJwxOwtMJf1BzHHju5g6WRgFQEkg7ErcLj3+4mq6yOx88Zhd5koKWlpmO3v0m/n/9KjSWebclz8RTmQ2H+EUcCdTd0OhgWXsew8DouTipgV9Rk3t3o4+b1CQywebkorYqL0wI0HB+qmiOoG3sdYfuWYP/hT+hr8qibdHenTU0hkYREESiKYgJeBwYCFuARIcTnoZBF0nEs3lbAV9sLmTIwhso6D8syipqOF1BVJu57iZT9b1KRNI2ttkkE9IdxJPcS7EY/1463M6bmJ9ZWRvFNaRxPZMTyyt58zu8XzaVp1STVh6UaLbhPfQrvT/MJX/8cpoL1VM+aTyAiObQXEWIa+wg8Hg8vv/w8K1euoKSkiJgYB1OnzuC66+YRGRl5yLwyMnbw6qsvsHXrFsLDwzn//AtpvD5LSz6DxvsfffQhNm/eyP33/5Vnnvkne/dmkpSUxIUXXsKcORd04B3oHELVI/g9UCqEuExRFAewEZCKoAeTU+Fk/rI9pERZmT64hWHvaoBpe55gVP5HlCqXsE+5jsD2pV0raDfAqIMp0ZVMjqpkt9PGL7rR/HdPgLey7JycWMfvB1YzNtqD0WjEN+1u3PHDMK96Asf7s3DNuJ+aoRd16DxFPZlnnnmCpUu/Zu7cS0hJSSEzcw8ffbSAnJxsnnnmxRbPy8zcw623Xo/dHsmVV16D1+vl/fffweNp/4R9lZWV3HXXrUyePJXTTz+TH35YxpNP/p3q6mouu+yqdufbFYRKEXwILAz+rwM6ZtimJCQEVJWHvxbogLOPSmp2ErmRDj8jNv6NuPwvKRhyCbopfyDOXdejfQFHik4HQ211XDIjlp2bdvFhjp3Pcu18XRDOqEg3V/pLOEm3EbPehnf8zYTtWEDY0rsxZHxB9fGPEogaGOpLCDlLlnzFGWeczQ033HxgX1iYjTVrVlNXV4fNZmv2vNdffxXQ8fLLr5GYmATAiSfO4qqrLm23LDU11cydewm3334XAHPmXMDtt8/jzTf/wznnnH/YHkooCYnRUQhRI4SoVhTFjqYQ7guFHJKO4YMNeWzMrWLejHSiw5qZDVFVUTY/Qdy+L8mOOY6dDMS781tMqtT/oCnSqspyTrVn8/TQHVyenEuJC/5vaRmzlqXwVEY0WWoydePn4T7ujxgL1uP430xs654HvyfU4oeU+PhEvv9+KYsXf0F1tTbZyHXXzeM//3mrRSUQCARYs+ZnpkyZekAJAAwYMJCJE4/MGX3ZZVce+N9gMDB37sW43W7WrfvliPLtbELmfVIUpT+wDHhbCPFeqOSQHBnZ5U5e/HEv09IdnDy8+fmDjt33Mgl7PyYneiJZjhldLGHPwmoIMCu2jH8M3cl/zopjbLSLt/fZOfPHfly9NpGvw2ZTe8GH+NOmEb7mH8R+MIuI7K+A7r/2eGdw991/IhBQeeyxhznzzFncfPN1fPDBu9TUtNzXrKysxOmsIyUltcmxtLSB7ZYlMjIKh+Ngs2hqahoABQV57c63KwiVszgRWALcIoTo3HkDJJ1GQFV55BuByaDjz7OGNjv/iWPH26Tuf4OSAWeSaRwtbdutRK+DyakWzImZnOcwsqI8hmXlDm7/Mp+UMB+/G3AeF4/sT3TmZ4R9cR26078AfwoYOm6xkp7AhAkT+eijRfz00wpWrVrJL7/8zPPPP8MHH7zHa6+9Q0xM0+nI699Tt7vpeITGzuKWaG7CN5OpaXVan06v794RX6GS7l60CePvVxRlefAv7HAnSboHbhXKPX4+3JzPhtwqrp82CJPF2CRUNK30R1J+fpB9jmlkj/mDVALtJNrk4+yEYp4cJph/qoNYs48nMmKYtmkWD0Y8TEHSSeh8TgxlAp2zFDp6Cu1uisfjYdu2rdTUVDNr1qk88MDf+Pzzb7jpptspKirku+++afa8qKgowsPDycnZ3+RYXl7OQdv1FbjHc7AJrrS0tMm5ZWVl1NUdPLX6/v1aGfU9g+5KqHwEtwshkoQQJzT4c4ZCFknbqfP6Wbwln5d+2EtKlBWzDpZlFOEN/FYBOWp2MivjXpyOUXw7/DFt3WDJEWHQwaz0MO7uv4uH0nczNqKK93JimZJ1DRX6GAIGC4bqHAzlu9B7qtHRfUYTdwZVVZXceONVvP32Gwf26fV6RowYGfy/+UFrOp2OGTNOZM2a1WRm7jmwPz8/j9Wrfzoobb2pZ9eunQf2FRUVsnXr5ib5qqrKxx8vOLDt8/n48MP3iIiIYMKE7j02Rn6dknbxw+5SnF4/F49IOcgkNDJWJcqVj7LuLlSzndpZT3C0SyWuh48W7m6k25zMs+3noqR8vi2Npc6fzDZPEgn6ahJ8Zegr9qKGxeAP75gV0LojcXHxnHLKaXzyyUJcLhejR4+hsrKSjz9egMMRy0knndziuddeeyOrV6/k1luv58ILL8VgMLBw4QfYbLaDWv8zZ57CO++8yUMP3cuFF16Kx+Pmo48WEB+fwP79TadCefPN/1BQkM+gQel8//23bNmymT/96X6s1u69cpxUBJI2s6uohg05lUzoH02i3XLQsXB/NWnLb8XoLmFTyu9J2L+BmqJqkifMDJG0vRuHyceFSYXERBgwW32UeyIoDYSTrCsjxlmOwVVNePRATlTiu41pzmbquAV0/vjHv9CvXyrffvsN3367hLAwK8ccM5Hrr7+J6OjoFs9LTEzipZde46WXnuW9997CZDJz1llzAA7qYQwZMpS//vVx3nzzP7z00rMkJCTy+99ficvl4qWXnm2S79NPv8CTTz7O4sWLGDhwEI8++k+OP/7EDrvezkIqAkmbUFWVl3/ci81kYPrgpksj9t/yPNGubDISz6LGmoycWb9r0AFhOi9hFi/OgJ4KXwxlATv91BJs5bswGsJR7SkYzD3bFffCC/86aNtisXLNNTdwzTU3tDmvtLQB/P3vTzfZ33BMAmjjC048cVaTdJdeelmTfWPGjOOttz5osyyhRioCSZtYtquErXlVnDYiAWujlp0j4z3isz5lf/QkiuyjQyShJEwfIMzswRIRTaUrnJq6cmL9pegrdlGpj8FgTyDMYjl8RpI+g1QEklbj8QV4bsVeBsbaGNvv4FGSSZUbSNnyAJUJE9lrPyE0AkoOwkAAq68azEaqAgmYvNVEB8pwV1RTaEgg3B5NuNkgl7iUhG5AmaTn8cGGXHIrXdwwbRD6BtNIRLjyOGX7H/HY+5M14QE5S2Y3xKjXoVoiqTU5MOp1pARy0VdkkVtaSZXL2+r4eUnvRH6xklZRXufhtZ+zmZbu4Ji035xwJl8Ns7f9AT1+9p78Gn6TPYRSSg5HQG+B2HT8tgTsOicDAtlQuZ/9JZWUO6VCaC9/+ctDPXpVM6kIJK3i1VX7cHn93D7jt7V0daqfmRn3EV23j6XD/44nSq6z2yNQVer8BmrN8XgNNmJ0NQxSszFU7Se7pJKyOg8BqRD6FNJHIDkse0pq+WRzPheM7cfAWBvlHm2g0un5z5Fa/hPZY+6k36DRJKrFPX51sb6EqtPjNkbiMYRj0XmJcVcSo9ZQXh1BZk0cjggr0WGmQ64yJ+kdSEUgOSzzf8gk3GzkuuMGHNg3vOBTUve8R27UMWTVWWDb171udbG+gqozoIY78NviUOtKiHFXYMfJ/qoEKpw2+kVam0SISXoX0jQkOSRLd5Xwc1Y5lx6bimrQU+7xY8n9iWm7/05lwkT2xDWNr5b0PHQECHjrcGHFaY7FoIN0fT6R/gqyyuoor/NI/0EvRioCSYvUenw8/d1uEiLMRFmMLMsoYtO6laQuvZ7KsAHsnfCgjBDqhfh1JurMcfj0FpIooZ+hkvwqN7mVLryBAL6ASkDqhF6F/IolLfLKT/sorfVw2ogEDHodEa48Tt92O36zncWjnyNgigi1iJJOQkWH0xRNwBKNI1DKAGM5VS4f2WVOKp1e6UzuZUgfgaRZthdUs2BDLmcelcTJg61EuvNRNtyKUXVRcfILjFTtciK5Xo8Of0Qy7oCeKG8ZA0069nmjKavzEG4xYGxhdk9Jz0P2CCRNcHr9PLA4g9hwM1dPGUCEt5yBy+Zhrs1la8IcXEWZ1Gz7Wi412QfQ60ANj8dnjiHSX8ZgSwX+gEp2uROXt3dPc92XkIpA0oQnv99NdrmTv84eTrhJZdD6vxLpymVH4jlUhfUPtXiSrkQN4HdW4dRZ8Rps2LxlDDGXgwr7yp3Uunt+Y6C8vAyns33Lodxyy/VccMFZHSxR1yNNQ5KDWJJRxOdbC7l6Un8mpIRj/eYm7AU/sSv+VEojlFCLJ2kHEeYAet9vK2eZfF6MevdBaUx+LyoeLPqDbf8HpTUbMKoWrIEKxoW5KQ9Y8deBzmcgzKRv9TTXAVMELsKP7KI6iNWrf+Lhh+/jjTfeJSysZ8/MeiRIRSA5gCiq4dEluzgqOZLrJqVgX3or1r1fsX/0LeS75NQRPRW9rw6/+G3ZRoMlDL/74Baw3mwFvxt/o/VGG6f1Ayb8GHw1RBmsVGCnzAsWk54oqxGD/vDKQD/sZDB1D0Wwfbu21GVfR5qGJABkV7m4/eOthFsM3DczBftX12Dds4jCSfdTPHhuqMWTdCP8pgj8lhj0fjfR/nLCjQHc3gAltR5c3kCoxZO0A9kjkFDl8nLXx1upcfu4cZyN9K8vxVYjWDHkz0SPugZK94VaREk3wqADjFa8ODC5K4gOlGEzR1Dut1Je58Vq0mO3GjG2onfQVi644CwmTZrCmDHjePvtN8nLyyEhIZG5cy/h/PMvPCjtpk0beP31f7N9+xYARowYxdVXX8+4ceMBePTRh/jqq0UAzJ17NuPGjW+y8E1D1q5dw2uvvcru3TtxOGK5/PKrmqS55ZbrKSjIZ+HCLw65/5ZbrsdgMDJ37sW88srz5OXlMWDAAK644hpOOKHrV/OTPYI+TlmdhxsXbCan3MkdQ0q4afe1OJx7yZz0GBGjTiNRLSbOJmPGJQ1RwefE7/fhNoQT0Bkx+6pJ1FUSaQG3L0BJjYdqlw9/J4w8+/nnVcyf/yQnnjiTW2+9E6s1jGeeeYLVq1ceSLNy5Q/ceusNFBYWcMUV13DFFddQWFjA7bfPY+XKHwA455zzmDFDW0byttvu5Iorrm6xzLVr13D33bdRU1PDddfNY+bMU5g//0mE2NHu68jKyuT+++9h3LjxzJt3Kzqdnvvuu4clS75ud57tRfYI+jCF1W5u/nAzhdVOFoxez9hd8/HaktgYfym1pZVQKucPkhwaVafDY7BhCHgxBVzYAyVYLVFU+S3UuP3UevyEmQ1EmA2t8h+0hqKiQt544z2GDBkKwIwZJzBnzmyWLPmaKVOm4fP5ePrpJ4iLi+e1194iPFwb+HjOOedz+eUX8dRT/2Dy5KmMHj2GwYOHsGLFMqZPP4Hk5H4tlvnKKy8QGxvHq6++fiC/Y4+dxG233UhUVMtrIx+K0tISbrvtTi688FIAzj57DldccQkvvfQss2adgl7fde102SPoo2zIqeSKdzdgrM3jp6TnGL/zKbId0xHHv0qtJTHU4kl6GH69iYAtjoDejMlTQYxaQaxVxWzUUef2U1TjoaLOi8d/5D2EtLQBB5QAQGxsHA6Hg7KyUgB27sygqKiQ88+/8EClDWC32znvvAspLi4iI2N7q8srLy9DiB3MmnXqQfmNHz+BwYOHHuLMQxMREcG55/7mf7NYrMyZcz4lJcVH1NNoD1IR9DECqso763K4acEGLtJ9yzeWe3BUbKJm6l8on/oQMVFy2ghJO9Hp8eitePVh6AMerJ5SYvRO4iJM2EwGXL4ApTUeKp1eKl3tn6YiOjqmyT6TyYzfrw1wy8/PAzSF0ZiBAwcCUFCQ3+ry6tOmpKQ2OTZgwMBW59OYfv1SMZlMB+1LTU0DID+/9fJ1BNI01IfIKq3j0aU7qc7bwSL7mwz3bMMTOYiKYRfg1UVQs/0bkid0vaNK0rvw682oOiPGgBODtxq9z0nAGInBYsHr1yaty61wYTMbSLJb2jzF9eHWWD7ULKmBoM+icQXcmvLcbneTY4FA66KkmkvXnAz16QyGrm2jS0XQB6hx+/jvL/v5cP1ebjIt4kbrJ6iqhZ0Jp1NgHwMVOtITpENY0nEEdHo8hnAMej1GbzVh3jLMOjMeQwSYDURhpMbjZ29pHTE2E/ERlg7zISQlabb+ffuymD794GPZ2VoEXEJC682fSUn90Ol05ORkNzmWl5d70LbBYMDr9TRJV2+2akh+fh6qqh6k2OrLqO8ZdBXSNNSLcXr9vLsuhzn/WUPuuk/41nYfN7OA2vTZbD/pbQoix7Z6NKhE0h70RgsBWxw+cxR6AoT5yrB5yknWl5EebSQqzERZnZfM0lqqXN4Omd56+PARxMbG8cknC6mt/W1axNraGj755ENiY+NQlBGAVnHDoVv20dHRjB17NEuWfHVQhb5162Z27sw4KK3DEUt5eTklJcUH9mVk7CAnZ3+TfMvKSvn++6UHtp1OJ5988hGpqWkMHjykjVd9ZMgeQS+k0ullwcY8Pvg1h6M8G3nf9hkj2I7flkbVpPl4+k8juq6Gij2hllTS+1HB58YXAJ8hHIPqxaTTYXaVgquEJIMNuyWSYq+VnAoXkVYfSZEWjEcQMWM0Grnjjrt58MF7ueaayznrrHMA+OKLzygtLeFvf/vHgYic6Ggt4ud//3ubyZOPY9q045vN85Zb/sDNN1/LDTdcxbnnzsXlcrJgwXsHzq9n1qxTWbr0a+666zbOPfd8ysrK+OijD0hNTcPn8zaR87HHHkaIDOLi4vnyy88pLi7kiSfmt/va24tUBL2Iwmo3/1ufy5eb93GCfzWf2r5hEHvwGOPYFXMa+ZFjIbOQ9JolMiS0DxEw2jAopx7Y1pstGDwH27sNZguq34OhUVRPZ6R1G214w+wYPFUYXRXEeAuIQYfbYKHcbSOvxEZEhJ2YMNNh/QEtceKJs7DbI3nzzf/wxhv/xmg0MnLkaP785/sZO/boA+lmzjyV5cu/Z/HiL9iwYX2LimD48BG88MK/eOWVF3jjjX9ht0dy9dXXk5Gxg82bNx5IN3XqdO688x4WLPgfzz77FP37p3HXXX9m48b1rFq18qA84+Liue22u3jxxfmUlpagKCOYP/+lAwPeuhKpCHo4/oDK6qwyPtlcQPHeDZyr/5GV5h+JMFThjhjM8vj7iVJmUJ3xfahFlYSIGo8e+C0azGaMpM5VdVAaq8GO6q/F3chE0ilpPWAN1+PXm3CbYtGrXox+Nya8JKllQBm+GgN1NWHoLRFYbJHojFbQ6ZqM2K2nuf0TJkxkwoRDN3jsdjvz5790yDT1jBgximefffmw6c47by7nnXfwtCwnnTSLO++8p0naGTNOYMaME1pVfuFMAk4AACAASURBVGciFUEPJafCyVc7ili9eRtTnMu4z7SKYeZ9qDoDnrQZVA2/AE+/Y4ksdRJrU5HTakm6KwGdCY/RhC5c8yPoPDUE3DWEeeswuWvAXYAfIwFzBHprFKo5AvSy6upI5N3sIXj9AbbmV7NuXzE5O9czsHINJxs28if9LvQmlQL7aLIH3kFOjYrPYIPdOaRXVVJTVC1DQiU9Ah0BAp5a3F4V9OFgCafKHyAQUPn/9s47PKoqbeC/Oy09tIQSej2ErhRFUIoKomBZddW17id2dG0riIuIoqhYUVHsurZVsVCkSFEQpIhI59CbhJqeSabe7487xAApk2Rqcn7Pkydz63nvzHvve885b7F67CQ68zA7s9HR8Jjj0GPqoMUmgzlGOT1Uk7AYAiGECZgKdAccwEgp5Y5wyBKJuDxedh23s/PgUXIObMJ7bCvJedtpr+/lPtMOErUisEJhvY4UtbqNonbDOOisT0q8jnv1vIobUCiiBJvZRGxyEh6HjWxXPZxuF1ZPEYluO3GeDLBn4MKKyxyHbonDZInFZLVhNlvBZAY0ZST8IFw9gsuBWCllXyHE2cCLwGVhkiWk6LqO3eEgL/sY+dlHKcw9hj33KI7sQ5jy/yS+MIN67iO00o7RXztWfJzLbCU/oRWetOHsTkrH2vwMdm1cDXnQ5s8t6s1fUaMxaToxmocYq4ZujcNpa0SB24XJmY/NU4DNY8fmyTVeK0ugo6FjQtdO/JlBMxtGQjMZQ0xmK5rJCmaLsaxZoQKvJV3X8erg0XXcHi8uj47bq+PyePF4jW06RoyAWdOwmDSsZg2r2YTVrDHltWmYIshAhcsQ9AfmAkgpVwghegWjkfUHc/lo1f7iUHZdh4sKvqWzYy0aOug6xk9hqIuG/tdn3zEnrzdOYsJbvK14ve8YE17MuLHobix4MOsuLLrbWIcbi+6hoVZ6rVc3ZrLNDShIbIQr8QxkQlPqNW3D3n0HKLTWA81Em5Qk9h/Jo3375GB8ZQpFxKMBcVYdXXfgsJjRLckUAQVeHZPFBl4XuseN7vWi617QvWher+/e9GLCiRkvZs1YNu7jkzH2Nu5+LyZOflLooJ9YBrPvzxBO46/H+4knhI7xONF854YiTnRUfPtrJ7ZqZJnr49Ri0NAwmaBRUvXcaf1BKy8cO1gIId4Fpksp5/iW9wFtpJRlFUA9Cqik+ApFGWzcuKlrWlqr6C8grPCbgwf3WLp06byhgt1aAqkVnStcPYJcoGTtQ1M5RgD8uBCFojbjcrl2ud2ezKr63SuiC13XcblcKUBARlPClWJiGXAxgG+OoCKrplAoykHTtGyPx628AGsJHo/bqmlkBep84TIE3wJFQojlwMvAA2GSQ6GoEei6vq6oyB4ZFeEVQcduz0/SdX1OoM4XljcIKaUXuDMcbSsUNRGv1/NMbm7mbIvFFh8TE2tXQ0Q1D13X8XjcVrs9PykvLzPb6/WWXWC5koRlslihUASeNWvWXGQ2Wx7Rdb01oCxBDUTTyNJ1fY7X6327Z8+eewJ2XmUIFAqFonaj6hEoFApFLafcOQIhRANgHHABkAxkA0uBCVLKI8EXL3hUlOZCCHEbcAfgBiZKKWeFRdAg4cf1PwBc61v8QUo5IfRSBgchRAqwCGiH8TK0B1hICb32fT+zge+llG+FSdSg4MdvPwwYjzG8tAa4R0pZI4YO/Lj2h4B/YMR9PSOl/DYsgoaYinoEHwG/AudgBCb0xzAEnwVZrlBQnOYCGIOR5gIAIURj4D6gHzAUmCSEiAmLlMGjvOtvA1yP8bufDQwRQnQLi5TBYS6QCTQGBgI7OV2vJwKnV0mvGZT32ycBk4HhUsqzMIxkSjiEDBLlXXtd4F9AX2AIEPoKMWGiIkOQLKX8n5QyV0qp+/5/AdSEh+JJaS44OTCjD7BMSumQUuYAO4Ca9CCE8q9/P3CRlNLjexO0YkTF1xQaA2/59HkF0KOkXgshrsJ4I5wbRhmDSXm//TkYcT0vCiGWAoellEdPP0XUUt61F2BkMEjw/flXmb4GUJH76BEhxOMYX1wORjTwJUBGsAULAckY13QCjxDC4otwPnVbHlAnlMKFgDKvX0rpAo4JITSMt8O1UsptYZEyOLiAS4QQuzC+A7MQYgKQIYTogjE0cBXweBhlDCbl6X4KMAjoAeQDS4UQv9ag37+8awfjJWgzRvqgSaEWLlxU1CO4AeMhOBp4HXjUt3xzkOUKBeWluTh1WxLG/EhNotw0H0KIWOBT3z53h1i2YDMD423vhF6feDjcDNwENMWYQ7gFeFAIcVF4xAwa5f32x4HVUspDUsp8YAmGUagplHftw4AmQGugBXC5EKJW1HQt131UCNGirG1Syn1BkagUvF6v7vFE1lzVvn17cLvdWCwWWrRoFW5xIp5du/4qN9GmTbswSgIZGQdPWi4qKnSNGDGiHZys10KIJ4BDwZwsjkTdVtQcrFbzMQKQdO5/GJlWUzCs6AagM3AYCFmFZY9HJzvbHqrmKmT37l088si/ipcnT55Cq1atwyhRZHP11SNOW/fVV6XXng0F99//AJqmkZ2dTWFhAS1atHBg6PYxoG0oZYk03VbULFJTk/zK2lzu0JCUsq+U8hxgE9BBSjkE6AAcqL6I0csLL0w6ZfmZMEmiqArTpn3AW2+9T+vWbfj882/44osvJJCGoefFSCmfqGmuo4rAc+hQBitWLAu3GNXC34CyZlLKPAApZQHGOFqt5ciRQyctHz58qIw9FZHM0aNHiI838rQpvf6LOXNm8vrrL4dbjKjhlVee58UXn8XrjV4nI3+Tzs0XQvwM/IbhWvld8ERSKEJD795nMWrU7bRv37bZV199tRSl1wC8/76Ry2zUKJUU2B927txR/L99+w5hlqZq+GsIxmNE4nUAPpZSrgueSApFaLj11jvYsWM7Bw/utn/11VejlF4rKovd/tf8zubNG2q8IdgAzATerUH+xIpK8vPPi1i06MeAnGv8+Ecrtf/gwRcyYMDggLR9gptvvpZ+/c7l2muvLlBGQFEVYmL+iq2tWzd6A9H9nSPoDqwCXhJCLBBCXB9EmRSKkPDhh5+Tnt6ZiRMnNld6ragKZnNx2Xq6du0eRkmqh189AimlE/haCHEIuB/4D0awUVRT095wg82AAYOrJHNp7qMTJoQ/aNNqtTJo0AU0aZJyaNWqVdn4qddCiPMxchG5gCPATVLKUn1AfbE43aWUM4UQZ/nO/5WUsnIKo4h4EhISwy1ClfGrRyCEeFwIsQG4HZgipUwPrliKmsTIkXedtHz77feESZKT+eCDd7jppmv44osvUqicXk8FLpdSngdsB0aWs+9gjOSFYCQwfDWSjUDJANPMzONhlCT6KCqK3nRc/s4RZAHnSilrVJqFmvaGG6kMHXox7777ZvHyhRdGRsaGpKQk3njjXdq0SduDkUrhJIQQTfgrI+kmoIuUciAwUEp52LfeQhkJ+YQQZowMl/FCCDvwf4BTCHEgUtMbHzv2V365LVs206/fuWGUJro4duwIdepEZ0oyf+cIfgbShRBnCSEWCiEuCKZQippHo0aNgcjpDQD06NGTvXt3s2LFioQy9Hoc8JmUchCw/MRKKWUGgBDibxgJ2j4u7fxSSg/wrO8cTwIfAi9FqhEAOHLkcInPKj6mIhwOR/HnHTui14/GX0PwFkYRh/8Aj1FzszL6xanpEcKZLiFaaNAghU6dukRMbwCMCHGr1cbUqVObULpet8ZwkgBYXHKDr3DPQxjpuqN3TOAUtm7dXPx5y5bN5eypAFi79rfizytX/hpGSaqHv4agCKNrbPPl8PYETySFIjTYbDZat26Dy+XSytDr9Rj566FE3nohxGPAucAFUspjFTTjJUpKwu7du4dvvvkK3WzDa0tk7drfWLr0p3CLFbG4XC6+/vpzLCadlFgPGzasY9OmDeEWq0r4q6A6Rvf3ByHE3zG8JWo1nTp1oVOnLqo3EMVomsbEiePp379/Thl6/SxGdbYTKakRQjTCCLBMA+YIIX4SQtxF2WwALhNCXFvOPmHH5XLx4ovP4sSMN7YOXlsCnqTGvPnmayqFSikUFOTzwgvPsHfvXhrFeagf46VRvM4LkydGpTHwd7L4GozUEnMwSvtdCyCEaCml9Cu7nUIRaUyYMIktWzYyfPjQI1OmTDnKKXotpcwCLvOt64hR1ewwYPO3DSnlWkAEQfyA8r//fUpGxp/YOwzBlrEegMI2A7Bs/IapU19l3LinsFj8fVzUXHRdZ8mSxXzy3/fIy83lpg75rDpiBJU93C2bF9bDhAljGTRoCNdffxPJydExeexvHMEx4AffYsmx0g8w3OMUiqijbt269O3bn71797bFeMtHCAHQXQixREp5mT/nEULYgPmlbJJSyjtK2f9xfPfNxo3hH4efPXsG338/HWeqwFOnGfgMgW5LwN7ibDZvXsobb7zCPffcX6uNQUFBPq++Mpm1f/xOm2QP9/fMo02yp9gQNIr38mSvTL7bHc/8xfNZvWoZDzz4aFQEmlX3V9UCIoVCEUZat2690+cWCoAQYvGpRkBKuRWjN3wavoDLUreVsf+TwJMALlf4qtLk5ubw0UfvsWTJYtx1W+Jo0fe0fdwp7XE47fzyy88cPnyIu+66j+bNy6xXVWM5cuQwzz/3JAcO7OPGDgWc37QIUylPvzgLXNfezrlNHEzd7OXppx/nzjvvY+DA80MvdCWoriFQpZUUNZEardcul4sFC+by+RefUFhYhCOtB860HqCVPmXoTOuONyaJ7bt/5eGH72PYsOFceeU1JCUllbp/TcLpdDJv3g989eUnaB4HD3bNpWuDiqdImyV6+M+Z2by6IZk33niF1atWcP0Nt5CW1jQEUlee2tvPUyhqGXl5efz880JmzZ7B8WNH8SSnUdT5LLxxFSdLczdoQ35yE2x/rmH27O9ZuOhHhlx4EUOGDCuOEalJ5OTksGDBXObOmUF2Ti5d67u4WeTTMM7/mgPxFp1Huufww75Yvv99Bat/W8lZZ/Vl+PDL6dChI5oWOQMqamhIoTidGqXXW7du5ocfZrJq1Qo8HjfepEYUdRiCJ7kpVOJhpFvjcLTqj6thJ1wH/2DGzG+ZMeMb0tM7M2zYcPr06XtSErZopKAgny+//Jwf5/+Ay+2ma30Xd/aw06m+u+KDS8FsghGtijgvzcHcfXEsXrOcFSuW075de26+5TaEiIxsPeW6jwohnvb9L2vSbFHAJVIogsy0aW8AlOcjXyP02m6389LLzzNu3Gh+Xb2awgYdKOh8GQUdLzEmhav4RuqNr09Ru8Hkd/s7jqY92bJ7Py+99BxjHn2IjIyDAb6K0JGRcZBHRz/AnDkzOCc1n0lnZfPvHrlVNgIlqWPTuaadnVf6HufmDvkcO7CdceNGM2/e7ABIXn0q6hH8XQhxELjX5z9djJTybSnlU8ETTaEIDosXLyAlJZXp0/9HZmYmmuZOef7552+HmqPXO3du59UpL5GR8SeOpmfibNQZzNaAtqHbEnCmdcfZpCuWzN3s3b+SMY8+xB23303fvv0jauijItasWcVrU15Acxcy9owcRN3qP/xLI9YC5zdzcE5jB29tTuLdd99i165d3HrrHdhsfnslB5yKDMH1GBkTYzi5nmuNnkxT1Gwef/wpVq1agcvl4vjxY+i6y4qh31Gv1wUFBXz++cfMmz8HrPHYOwzFk5wW3EY1E+4GbclLSMWz62defvl5Fi76kdtG3kXjxpFfBnrhwvlMm/YaLRM9jOqeW6l5gKoSZ4F/dc1j+q44Zi6az6GMA4x9bAIxMbFBb7s0yjUEUspVwCohhC6lnHhivS8KU6GISk5EhQPccstIUlOTMsaMGTMh2vX6t99W8da018nJycaZmo6j6ZlgCd1bph6bTEH6JVgPb2HDprU8+OAorrvuBi6++NKInTv45ZefmTbtdbrWd3Ffl1xsIRTTpMHVbQtpmuBh2ubNvPjCJB56eOxJVc9CRbmGQAgxHCOX+nVCiHjfahNGtOWX5RxnwsjZ3h0jWd1IKeWOEttfxcjhkudbdZmUMqeqF6FQVIZly5ayYcM6FiyYh8PhwOt1Nv3000+fJUr1Ojs7iw8+eIfly5eix9fH3nE43sTUYDdbOpoJV+POuOu3Inbvcj7++H1++WUJd999Hy1btg6PTKWg6zozZnzLp59+QIc6bu6tohHQdchymCh0ayz6M4ZBaY5KT72c09iJw5PPh3/8zpMTxvLI6HHUqVO38sJUg4qGhtYBDYBCQPrWeYEvKjjuciBWStlXCHE28CK+UH0fPYGhfiTsqpAPPniHPXt2Vfc0leZEm5WtTBYIWrVqwz//eVuVjq2N3xec/J21a9ee3NwcYmJiaNGiJZrmPpFUMWL02h8cDgcLFszjf19+RmFREY60M3A26QamarzW6jqa047mcWI9shVXqqjSpLJuS6Cw3QVYMneza/9KHnnkAYYNu4Qrrrg65A+5U/F6vbz//jTmzfuBPg0d3J6eX+WewKI/YzhcaBz8oUxE1405gMoyqKmDBKvO21u28dijDzJu/DMhdcutaGhoP/CREOK/UsrTBs6EEG9KKUtLuNUfmOs7xwohRMnMjSagPfC2bwL6PSnl+1W9gD17drFJbsMTX7+qp6gSmteYeFu/PyT3fDFme2a1jt+zZxd7tq2nRWJoE8jW8Xlkeg+uDWm7APvyT77LGzVqzLBhwxk69GJMJhOpqUnHb7zxxv+e2B4Jel0ex48f48cf5zJ//lzy8nKMeIBOQ/HGVf8Baz26FbMjF4DYvcsBHVfDKro4ahruBm3Iq5NGzIHfmD17BvPmz2HAeYMYOvQSWrduU215K4vX6+Wdd6ayYME8hjUv5Jp29lIjhP1l7XHbactVMQQAfRo6aRCTw4vrYfzjo5nw5HMhMwb+5hoqa/akrGRayUDJLrFHCGGRUrqBBOA14CXADCwWQvwmpVzvp8yn4YmvT2HHi6t6eFQRt/WHineqgBaJHsaemRsAaaKDZ35PLnW9yVSm93RE6PWpZGQc5MsvP2P58l/wej246zbHKc7Bk9S4yq6gp2LJ3n/acpUNQfFJYnG06o+zUVdshzeycPEiFi6cT3qnLlx7zfXF8zXBxuEo4o03XuHXX5cxoqWdq9oUVvtrc3q0cpcrS9s6bkb3yOa5P2DcYw/zyJjHadeuQ7XO6Q/BypOeC5SMPzf5bhYAO0bdVruUMg/DZzvyszIpFGHU60WLfuSBB+7hl1+XUZTakfxuV1PY/kI8yU0CZgQA8LrLX64GelwdHK36kdf9Goqa9WbLjt2MH/8o7703DY8nuD3UnJxsxj8+mhW/LuOatgUBMQLBomWSh7FnZGN2ZvP4uNGsXLm84oOqSbAMwTLgYgDfWGrJBN0dgGVCCLMQworR3f49SHIoFIEk5Hqt6zozZ37Hm29OwZnQkPwuV+FocRZ6TBTn+bHE4GrSlbwuV+Js1Jm5c2fx2msv4XIFp8yJy+Xi6YmPs3/vLu7vlsslLYsi1gicoFmihyd6ZtEywcFLLz0b9BoHwTIE3wJFQojlwMvAA0KIB4UQl0optwD/BVZg1EL+WEq5KUhyKBSBJKR67fF4eP/9aXz88Xu46rXC3v5CdFt8xQdGC2YLjhZn4WjWi2XLlvDUU+MoKMgPeDNLl/7E7j27uSM9jzNSoqemVrJN59/ds0mJ9fLfj99D14MX5hKUXEO+OYU7T1m9tcT2ycDkarYNGK5zZvvxgIydRwNm+3GysyPTJ7sGEXa9Bvj004+YO3c2zkZdcDTvHdghoLLwOImNjWX48OHMmjWLfI8z6E06m3TDa0tgy9alTHr2KSY88UzA4g5cLhffffslzRO99EoN/rUEmjgLXNSsgI+37WTdurX06HFmUNqpKI4gFhiJUbP4Y1/edYQQd0gppwFDgiKVImhkZ2eRlWcucwK1JrI3z0y97KziZYfDwaxZ32GzxXDRRZcUr48kvXY4HPzwwwxcDdriaNEnZO1qbifDLx3OqFGj0HWdL2fOC0m77gZtKfK4kFuXs23bVtLTOwfkvN999zUZhw7xULf8oNjRQrd2kuEsdAc+NcWANAfzDiTw7jtvMPmF14mLiwt4GxX1CD4Gdvj2+0UIMdRXvu8aYJqUMuz9rLp167Evz1OrvIbq1q04bbCibCZOHE+zZs3xeNzcffetvP/+e+b69etDBOm1yWRCM5nQTYHND1QRusXGrFmz0HWd2bNno1tCNxSlm4zHkcUSmGvetm0rX3/9Oec0ctA9SENCdrfG8OF/Gc4ls8uMR6wyVhPcKnKZtFbjgw/e5u67/xXwNioyBA2llH8HEEL8DZghhLiAGpamtzZRt249ku17ap37qKmE8czKyuSpp54F4OefFzFy5Mh2mzZtiiGC9NpqtXJO3/4sXfYLzrQeoZsbMNsosmcyffp0YzkpRMFfupeYI5to1KgJbdu2q/7pdJ1335lKvRgvN4mCAAhYOvEW/STD2cgSnHH8jvXcXNKykFmLF3DhhRfRvn1gy2BXNFlsE0KkAEgpvwGmA59iJKFTKKISt9tNdnY2AAMGDGbw4MFZRKBeX3nlNVhMGvE7FqC57OEWJ3h4PcTuXoqp4DjXX39TefEdfnP06BF279nNsGZ24oP0cAaIs+gUFRUxffp0ioqKiAtiWyNa2rGYYOXKXwN+7oq+8XHA0hMpqKWUrwBrMULpFYqoZOTIO7nnnpFkZh4HYNSoUUeIQL1OS2vKQw+NIcaZS+LmGZhzozfXf1loRbkkbJ2N9fhOrr32Bvr27R+Q82ZlGRH4jeNDG0EfTOIsUDeGYr0NJBWlmFgIpJ+y7mkhxDsBl6QamO2ZIfca0lyFgFG1KZQYKSZSQtpmTaNXrz58+unXJ62LRL0G6NmzN5MmvcCLLz1PxrZ5FLbshzs1+JGmocCUf5SE7fOJs1kY9e+x9OnTN2Dnzsw0DIHNFPWZxU/CZtLJyspE1/WA1nvwy31UCLGYU3K1C2GMUUkpBwdMmirQqlXo85XAX0nUWjVvHuKWU8J2zTWNe++9A03T8HhcHdatW7cIIkevS9KyZWuee/YlXnhxEuvX/UKR7sGV2jE47qQmS/nLAcKcm0HCzkWk1KvLE088TcOGjSo+yE927NjG29OmkBKn0zo5OAVmwkWf1EK+27ieTz75kBtv/GfAzuvvr7wRWA4sBnoDNwOPBUyKalDVLJzV5UQWzQkTJoWl/eqwLz/07qM5TuOhVccW+je0fflmWpWyvk2btnTp0o1Bg/rvGjhw4MtEkF6fSlxcHI/8eyyTJ09i3bpfsWbupqhZL7yJDQPajrtucyw5B05aDiSaI4+YP9diPb6Dho2aMG7ckwE1AgDvvP06mquAMT2ziQlyyI3NrJe7HGgub13I0SIzM2Z8Q9++/QKWh8hfQ9BJSnmv7/NMIcS/pJSy3CMUEUm4ehM5vh5UvbTQt9+K0q97z57dPPDAI6SmJrmllBGv1zExsYwdO54FC+bx+RefYN4yC09SE5yNOhkPbK36k6yu1I5YD21C8zhxNj3TSENdXXQdU8FRbIe3YM3ahdlsZvhlV3LllX8nLi7w3lBxcQkUuTX+OGZjQFpRUI3BGQ2crC+RgfSMBsELWtN12JFj4aDdgqYR0HgCfw2BJoS4FVgFnAsEPg5cERJUD+ovdF1n1qzv6Nmze9zVV199N1Gg1yaTiSFDhnHuuQNZsGAuM2d+T9aOhWCLx1mvNa76rfEmpFZ92EjT0G3x6MTjatixWrJqRTlYM3djy9yNVpiFLSaWCy++lBEjrqBBgwbVOnd53Pevh5ny6mQ+2byJr3cn0DPFQc9UJ13qOYkN8EjX4KYO5u6Po9Ct8bc2dgalVS0FdVnoOuzNN/P7URsrj8aSUWAiKTGR+++/m6ZNA9db8/dr+QeGB9GVGMVqbgmYBApFmBg/fiIffvgeCxfOawo0J4r0Oi4ujhEjruDiiy/l999Xs3jxQn7//TdshzdBTALOOi1w122BJ6kJBMAd0y90HZP9OJbsfdiy96LZjWhuIdIZOPB6+vU7Nyg9gFOpX78BT0x4li1bNvHTTwtZueIXlh0qxGICUddF1/pOOtdz0TzRU61aBGDY23oxXurFGEYhEOQ6NTZnWdmQaWVDZizZDqOdjiKdEecN4txzBxEbG9jaxv7WIzgE3HPqeiHEt1LKKwIqkUIRIho0SOGhh0aTmpq0AygunRZNem02m+nd+2x69z6bgoJ81qxZzYqVy/lj7e+4jmxBs8birNMcV71WeJKbBt4o6DqmgmNYs3Zjy94LRXlomoYQnejb9yr69OlLSkp4ymamp3cmPb0zt912N1JuYc2aVfyx9je+2GHMgSTaQCQ7aVfHRfs6bloluUNasxiMN/4jhSZ25FjYnmtlW46NA/nGb5QQH0e3M3tyxhk9OfPMXkGt7FbdjlJ4a84pFMEhKvU6ISGR884bxHnnDcLhKGL9+j9YsWI5q1avpOjYdjRrLI66rXCltMebkFItryPNkYf12HZiMndBUS4ms5nu3c7g7LPPoVevPiQn1wnglVUPi8VC585d6dy5KzfddCvHjx9j48b1bNiwDrl1E2t2HgbArEHzJA/tkp10qOOmQ1039WPKqslVNZwe2JVrYVuOle05Fnbm2cj3TSvExcbSoUNHzuvSjS5dutGmTbuAJd+riOoagprlpKtQGES9XsfExBb3FFwuF+vWrWXp0p9YvXolrqNb8Sak4GjcDXe9lpUyCKb8I8RkrDMqmWkaXbp047xzB9K799kkJiYG8YoCR4MGKQwYMJgBAwwP4ZycbLZt28r27dvYvn0rS7dLFhwwns5pCTrd6xfRu6GTtsnuKtnOXKfGyiMx/HHMytZsGy6fbWnWtCm9u3WiQwdB+/YdaNasRcge/KcSHCdhhUIRMVitVnr16kOvBr4l3QAACxhJREFUXn2w2+0sXfoTM2d9z+Gdi/AmNqSw+Vl4E8sfvtEc+cQcWI01czcJCYkMu+pazj9/SNiGfQJJnTp1i40mGHUg9uzZxebNG/njjzX8uGkjc/Z7aJKgc1GzAs5t4sDixwjb7lwzs/bGseZYDF4d0po0Yciw3nTp0h0h0klKipziQsoQKBS1iPj4eIYOvZgLLhjKkiWL+eTTjzBtmYmzUScczXqD6ZQ3Ul3HelQSe2A1FhNccfV1jBhxRVBSIUcKZrOZtm3b07Zte0aMuIKCggJWrlzO/Hmz+EDuYt6BeG5Lz6NtGcFqRW74bEcCPx2MJSE+juEjhjFw4GCaN28Z4ivxn+oagqyKd1Eooo4ar9dms5lBgy7grLPO4bPPPmLevB+w2I9T0O7Cv3bSdWL2/ort6Fa6du3BnXeOCnjwVzSQkJDA4MEXMmjQBaxZs5r33p3KxDUmbuqQz6BTPIWOFJp4eUMdDhaYGDHicq666jri4yO/qly1DIGU8spACaJQRAq1Sa/j4+MZOfIu0tO7MOW1F0nYPh8vGqARs28FtqNbueyyK/nHPwKTFTSa0TSNXr360LFjJ6a8OpkP/vidHOdf38mBfDPPrquL1xLPuHFj6dq1exilrRwVVSi7vaxtUsq3Ay+OQhF8vv/+m5OWNc2d8vzzz98OtVev+/U7F5PJxEsvPQvWeHSzDduRLQwffjnXX39zQBOcRTuJiYmMHvM4b745hW9+XkSTeDeJVp0X1tfFHFeXJ594JqDBXqGgIhPfEfg30BhoUuKvcZDlUiiCxr59e/j88/+SmXmc48ePcfToUStKr+nbtx/nnz8Ek8uOqSiH5i1acsMNtygjUApms5m77rqP9I7pHC60cMRuJsdpYsyj46POCEDFaagfFEJ0BOZIKVeHSKaoYMeObTidTkaPfoDnnns53OIoKsG99z7I3r17OPvsc0hP70xqalLGmDFjJoRbrkjgkksuZeHC+WjoXDxsRNjcGaMBs9nMjTfdytixD5PjMjFgwEDatKl+dbVw4M8cwU1AdDgIhxCn0/Az3rVrR5glUVSF//znSQoLa3DVryrSrFmL4s89epwZRkmig5LZPwcOPD+MklSPCmd/pJTHpJR7QiBL1PDvf59cPHr06AfCJImiqtStW5cmTdLCLUbEUXIYqEEDVQCpIkp+X23btg+jJNXD38I064AkoAg4ke1IA3QpZdRWSfn550UsWvRjpY87UZTmBLt27SjOrukvgwdfWBzZqAgPN998HXa7HYvF1Hn//v0nftSo1+tAoeYGKkc0x1b46z66HPhYSvmrEKIrxgRyePIZK6ISl8vFn3/uJysri3r16oVbHAC6du3GRRddwqBB/TYJIf5JBXothDABU4HugAMYKaXcUWL7bcAdgBuYKKWcFdQLUCgCRGUK0/wKIKXcIIRoIaUsM+dqtNwwJfONVIarrx5x2rpIyrMfiRw7dhS73c706V8wcuRd4RYHMArTdOnSDfBPr4HLgVgpZV8hxNnAi8BlAEKIxsB9QC+MXvMvQogfKzifQhER+Bshki2EeEoIMUII8Tywt4L9i28YYAzGDQOcdMP0A4YCk4QQMZUXXREtZGVlkp1tBOsuXryArKzICNxNTEzknXfeZObMmXX81Ov+wFwAKeUKjIf+CfoAy6SUDillDrAD6BYMuRWKQFOZwjR3A8OA9cBYACFETBlvPCfdMEKIUm8YwCGEOHHDKPfUCKeqcyoZGQfRdSOhp+Fye3+lJ2qDMacyfvzTfPvtVyxZsqQOsIuK9ToZyCmx7BFCWKSU7lK25QGRk4u5ErRu3Zbdu3eGWwxFCPG3ME0BMLmUTXOA0u7OGn3DdO3ajQ0b1hcvd+vWI4zSRD45OdmnLUeCx05cXBz/+MdNpKYm7Zs8efJbJTaVpde5GE4TJzD5dLq0bUnAyRceJYwePY68vNxwixE1jBhxBfPnzwm3GNWiuknnynIrqNE3zL33PsTtt99cvDxq1INhlCZ0VHVO5Z13prJo0Y+43W4sFgvnnz8kYuYJyqAsvV4GjAC+9M0RbCixbRXwtBAiFogB0oGNQZUySDRo0CCoNYVrGjfccAtXX31duMWoFtXNIlVWAY9lwMUAZdww5wohYoUQdYjCG6Zevfp07WoM/3br1iNivGAilauuuhZNM1TNZDJx5ZXXhlmiCilLr78FioQQy4GXgQeEEA8KIS71lXOdAiwFFgGPSSmLQiOuIpyYTKaodh0F0E6M3VYFIcQiKeVpr4glvIa6Ybxd/RPDMOyQUs7weQ3djmGInpFSTq+gqaNUPJEXUlwul/XgwYNt0tLSdlqt1tITkyuKycjIaJGbm5uanJx8tEmTJvvCLc8ptASKK6yUpddBIuJ0W1GjOEm3y6K6hmCxlHJQlU+gUEQgSq8VtQ1/I4tHAP+HMfYJgJTyYmBzkORSKIKO0muFwsCvHoEQQmIEgBU7gEsp1wVRLoUi6Ci9VigM/PUa2iSl/CmYgigUYUDptUKB/4bgeyHEr8CWEyuklP8XHJEUipCh9FqhwH9DcB/wPFHm769QVIDSa4UC/w3BISnl/4IqiUIRepReKxT4bwgKhRBzgbX4gm2klGODJlWEU1F2VcXpCCHOAp6TUg4MtywlUHpdAqXXVSNCdbtS+GsIZgZViuijzHTEitMRQjwC3AgUhFuWU1B6fTJKrytJBOt2pfA36dxHwRYkyigvu6ridHYCfwP+G25BSqL0+jSUXleeiNTtylLdXEO1lVKzq4ZLmEjHl0LEFW45FBWi9LqS1BTdVoagapSXXVWhiFaUXtdSlCGoGuVlV1UoohWl17UU1e2rGt8CF/rSEZ/IrqpQRDtKr2sp1co+qlAoFIroRw0NKRQKRS1HGQKFQqGo5ShDoFAoFLUcZQgUCoWilqMMgUKhUNRylPtoCBFCXAS0kFK+XYVjY4GtUspWlTzuQ+AL32Kl2hZC1AcuklJ+Vpk2FbUPpdvRjTIEIURKOTfK2u4GXAqom0VRLkq3oxtlCEKIEOIWoCPQCagDxAOPSSnnl7F/IvApUA/YUWL9T8CdUsqtQog7gcbAh8BXQAbQDJgjpXzs1LallGOEEP/ByDRpAd6UUk4TQkwCegENgHVSyn8CjwHdhRC3A3OAt4E4oBC4XUq5PwBfi6IGoHQ7ulFzBKGnLZACjACuo3xjfCewUUp5HjDNj3O3Am4BegODhRBnnrqDEOIMYBhwFtAH6CCEqANkSSkvxLhhzhZCNAWeBhb5utwvAFN8OddfAJ71Qx5F7ULpdpSiegShZycwC/gcsAJTytm3AzAbQEq5UghRWpZDrcTndVLKTAAhxEpAlLK/AFZJKT2AB3hICGEFGgohPgfygUSfbCXpCowVQoz2tRn1GRcVAUfpdpSiegShpz2QJKW8BLgZeK2cfTcDfaH4beeEAhcBTXyfS74ZpQsh4oUQZoy3os2lnHMrcKYQwiSEsAohfsRINNZcSnkdMBaji6wBXv7Ska3AaN9b0x0YXXWFoiRKt6MU1SMIPduBgUKIv2Mo4uPl7PsW8LEQ4hcMZXX41k8Bpgoh9gF/ltjfiaHEjYCvpZTrhDj5xUlK+YevPOMyX/tvAiuB/wghlmCUbNwFpGG84XUVQtwPPAy86fPwiAP+VcXrV9RclG5HKSrpXA1BCNEK+EJKeXa4ZVEoAonS7eCjegQRgBBiKoa3xakMk1IWhloehSJQKN2ODlSPQKFQKGo5arJYoVAoajnKECgUCkUtRxkChUKhqOUoQ6BQKBS1HGUIFAqFopbz/3R/fvI6w0WoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['m_q1_q2_tf_svd0'] = m_svd[:, 0]\n",
    "plot_real_feature('m_q1_q2_tf_svd0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['m_q1_q2_tf_svd1'] = m_svd[:, 1]\n",
    "plot_real_feature('m_q1_q2_tf_svd1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['tmp'] = m_svd[:, 4]\n",
    "plot_real_feature('tmp')\n",
    "df = df.drop('tmp', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.isfile('./../data/tmp/6_model.pkl'):\n",
    "    model = check_model(None, data={\n",
    "        'X': m_svd[ix_train, :],\n",
    "        'y': df.loc[ix_train]['is_duplicate']\n",
    "    }, do_scaling=False)\n",
    "    print (model.best_score_)\n",
    "    print (model.best_params_)\n",
    "    with open('./../data/tmp/6_model.pkl', 'wb') as f:\n",
    "        pickle.dump(model, f)\n",
    "else:\n",
    "    with open('./../data/tmp/6_model.pkl', 'rb') as f:\n",
    "        model = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "Fitting 5 folds for each of 90 candidates, totalling 450 fits\n",
    "[Parallel(n_jobs=-1)]: Done  18 tasks      | elapsed:  1.8min\n",
    "[Parallel(n_jobs=-1)]: Done 168 tasks      | elapsed: 11.2min\n",
    "[Parallel(n_jobs=-1)]: Done 418 tasks      | elapsed: 29.6min\n",
    "[Parallel(n_jobs=-1)]: Done 450 out of 450 | elapsed: 31.9min finished\n",
    "0.68117193104\n",
    "{'en__l1_ratio': 0.5, 'en__alpha': 1e-05}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred = model.predict_proba(m_svd[ix_test, :])[:, 1]\n",
    "y_test_pred_fixed = link_function(y_test_pred)\n",
    "\n",
    "if not os.path.isfile('./../data/tmp/6_pred_fixed.csv'):\n",
    "    pd.DataFrame.from_records(\n",
    "        zip(df.loc[ix_test]['test_id'].values, \n",
    "            y_test_pred_fixed), \n",
    "        columns=['test_id', 'is_duplicate']).to_csv('./../data/tmp/6_pred_fixed.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = plt.subplot2grid((1, 2), (0, 0))\n",
    "ax2 = plt.subplot2grid((1, 2), (0, 1))\n",
    "sns.distplot(y_test_pred, ax=ax1)\n",
    "ax1.set_title('Model prediction')\n",
    "sns.distplot(y_test_pred_fixed, ax=ax2)\n",
    "ax2.set_title('Model prediction fixed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Результат: **0.41092**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "data={\n",
    "    'X_train': m_svd[ix_train, :],\n",
    "    'y_train': df.loc[ix_train]['is_duplicate'],\n",
    "    'X_test': m_svd[ix_test, :],\n",
    "    'y_train_pred': np.zeros(ix_train.shape[0]),\n",
    "    'y_test_pred': []\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.isfile('./../data/tmp/7_model_pred.pkl'):\n",
    "    n_splits = 10\n",
    "    folder = StratifiedKFold(n_splits=n_splits, shuffle=True)\n",
    "    for ix_first, ix_second in tqdm_notebook(folder.split(np.zeros(data['y_train'].shape[0]), data['y_train']), \n",
    "                                             total=n_splits):\n",
    "        # {'en__l1_ratio': 0.5, 'en__alpha': 1e-05}\n",
    "        model = SGDClassifier(\n",
    "            loss='log', \n",
    "            penalty='elasticnet', \n",
    "            fit_intercept=True, \n",
    "            n_iter=100, \n",
    "            shuffle=True, \n",
    "            n_jobs=-1,\n",
    "            l1_ratio=0.5,\n",
    "            alpha=1e-05,\n",
    "            class_weight=None)\n",
    "        model = model.fit(data['X_train'][ix_first, :], data['y_train'][ix_first])\n",
    "        data['y_train_pred'][ix_second] = model.predict_proba(data['X_train'][ix_second, :])[:, 1]\n",
    "        data['y_test_pred'].append(model.predict_proba(data['X_test'])[:, 1])\n",
    "        \n",
    "    data['y_test_pred'] = np.array(data['y_test_pred']).T.mean(axis=1)\n",
    "    with open('./../data/tmp/7_model_pred.pkl', 'wb') as f:\n",
    "        pickle.dump({\n",
    "            'model': model,\n",
    "            'y_train_pred': data['y_train_pred'],\n",
    "            'y_test_pred': data['y_test_pred']\n",
    "        }, f)\n",
    "else:\n",
    "    with open('./../data/tmp/7_model_pred.pkl', 'rb') as f:\n",
    "        tmp = pickle.load(f)\n",
    "        model = tmp['model']\n",
    "        data['y_train_pred'] = tmp['y_train_pred']\n",
    "        data['y_test_pred'] = tmp['y_test_pred']\n",
    "        del(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = plt.subplot2grid((1, 2), (0, 0))\n",
    "ax2 = plt.subplot2grid((1, 2), (0, 1))\n",
    "\n",
    "sns.distplot(data['y_train_pred'], label='train', ax=ax1)\n",
    "sns.distplot(data['y_test_pred'], label='test', ax=ax1)\n",
    "ax1.axvline(np.mean(data['y_train_pred']), color='b', alpha=1, linestyle='--', label='mean(train)')\n",
    "ax1.axvline(np.mean(data['y_test_pred']), color='g', alpha=1, linestyle='--', label='mean(test)')\n",
    "ax1.legend(loc='upper right', prop={'size': 18})\n",
    "ax1.set_title('Train/test OOF predictions')\n",
    "\n",
    "sns.distplot(data['y_train_pred'][:ix_train.shape[0]//2], label='train first part', ax=ax2)\n",
    "sns.distplot(data['y_train_pred'][ix_train.shape[0]//2:], label='train second part', ax=ax2)\n",
    "ax2.legend(loc='upper right', prop={'size': 18})\n",
    "ax2.set_title('Train/train OOF predictions')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3703337544965632\n",
      "2181731531049.7178\n",
      "      fun: 0.00010042839608055774\n",
      " hess_inv: <1x1 LbfgsInvHessProduct with dtype=float64>\n",
      "      jac: array([0.00116664])\n",
      "  message: b'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH'\n",
      "     nfev: 54\n",
      "      nit: 10\n",
      "   status: 0\n",
      "  success: True\n",
      "        x: array([0.51660609])\n"
     ]
    }
   ],
   "source": [
    "mp = np.mean(data['y_train_pred'])\n",
    "print (mp)\n",
    "\n",
    "def func(w):\n",
    "    return (mp*data['y_test_pred'].shape[0] - \n",
    "            np.sum(w[0]*data['y_test_pred']/(w[0]*data['y_test_pred'] + \n",
    "                                             (1 - w[0]) * (1 - data['y_test_pred']))))**2\n",
    "\n",
    "print (func(np.array([1])))\n",
    "\n",
    "res = minimize(func, np.array([1]), method='L-BFGS-B', bounds=[(0, 1)])\n",
    "\n",
    "print (res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "w = res['x'][0]\n",
    "\n",
    "def fix_function(x):\n",
    "    return w*x/(w*x + (1 - w)*(1 - x))\n",
    "\n",
    "support = np.linspace(0, 1, 1000)\n",
    "values = fix_function(support)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(support, values)\n",
    "ax.set_title('Fix transformation', fontsize=20)\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('f(x)')\n",
    "plt.show()\n",
    "\n",
    "data['y_test_pred_fixed'] = fix_function(data['y_test_pred'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = plt.subplot2grid((1, 2), (0, 0))\n",
    "ax2 = plt.subplot2grid((1, 2), (0, 1))\n",
    "\n",
    "sns.distplot(data['y_train_pred'], label='train', ax=ax1)\n",
    "sns.distplot(data['y_test_pred_fixed'], label='test', ax=ax1)\n",
    "ax1.axvline(np.mean(data['y_train_pred']), color='b', alpha=1, linestyle='--', label='mean(train)')\n",
    "ax1.axvline(np.mean(data['y_test_pred_fixed']), color='g', alpha=1, linestyle='--', label='mean(test)')\n",
    "ax1.legend(loc='upper right', prop={'size': 18})\n",
    "ax1.set_title('Train/test OOF predictions')\n",
    "\n",
    "sns.distplot(data['y_train_pred'][:ix_train.shape[0]//2], label='train first part', ax=ax2)\n",
    "sns.distplot(data['y_train_pred'][ix_train.shape[0]//2:], label='train second part', ax=ax2)\n",
    "ax2.legend(loc='upper right', prop={'size': 18})\n",
    "ax2.set_title('Train/train OOF predictions')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['m_q1_q2_tf_svd100_oof'] = np.zeros(df.shape[0])\n",
    "df.loc[ix_train, 'm_q1_q2_tf_svd100_oof'] = data['y_train_pred']\n",
    "df.loc[ix_test, 'm_q1_q2_tf_svd100_oof'] = data['y_test_pred_fixed']\n",
    "del(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictors = df.columns[7:].tolist()\n",
    "print (predictors)\n",
    "\n",
    "if not os.path.isfile('./../data/tmp/8_model.pkl'):\n",
    "    model = check_model(predictors)\n",
    "    print (model.best_score_)\n",
    "    print (model.best_params_)\n",
    "    with open('./../data/tmp/8_model.pkl', 'wb') as f:\n",
    "        pickle.dump(model, f)\n",
    "else:\n",
    "    with open('./../data/tmp/8_model.pkl', 'rb') as f:\n",
    "        model = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "```python\n",
    "Fitting 5 folds for each of 90 candidates, totalling 450 fits\n",
    "[Parallel(n_jobs=-1)]: Done  18 tasks      | elapsed:   38.8s\n",
    "[Parallel(n_jobs=-1)]: Done 168 tasks      | elapsed:  3.8min\n",
    "[Parallel(n_jobs=-1)]: Done 418 tasks      | elapsed:  9.4min\n",
    "[Parallel(n_jobs=-1)]: Done 450 out of 450 | elapsed: 10.1min finished\n",
    "0.785762695095\n",
    "{'en__l1_ratio': 0.001, 'en__alpha': 1e-05}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred = model.predict_proba(df.loc[ix_test][predictors])[:, 1]\n",
    "y_test_pred_fixed = link_function(y_test_pred)\n",
    "\n",
    "if not os.path.isfile('./../data/tmp/8_pred_fixed.csv'):\n",
    "    pd.DataFrame.from_records(\n",
    "        zip(df.loc[ix_test]['test_id'].values, \n",
    "            y_test_pred_fixed), \n",
    "        columns=['test_id', 'is_duplicate']).to_csv('./../data/tmp/8_pred_fixed.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = plt.subplot2grid((1, 2), (0, 0))\n",
    "ax2 = plt.subplot2grid((1, 2), (0, 1))\n",
    "sns.distplot(y_test_pred, ax=ax1)\n",
    "ax1.set_title('Model prediction')\n",
    "sns.distplot(y_test_pred_fixed, ax=ax2)\n",
    "ax2.set_title('Model prediction fixed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "- Старое значение: **0.36629**\n",
    "- Новый результат: **0.3316**\n",
    "- Новое значение, но не улучшение: **0.33694**\n",
    "- Хочется верить, что это погрешность"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_real_feature('m_q1_q2_tf_svd100_oof')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "display(\n",
    "    eli5.sklearn.explain_linear_classifier_weights(\n",
    "        model.best_estimator_.steps[1][1],\n",
    "        feature_names=predictors,\n",
    "        top=30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.jointplot('m_q1_q2_tf_oof', 'm_q1_q2_tf_svd100_oof', df.loc[ix_train], kind='reg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Пробуем разницу вместо конкатенации\n",
    "\n",
    "<img src=\"./../images/mf2.png\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "del(m_q1, m_q2, m_svd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "m_diff_q1_q2 = m_q1_tf - m_q2_tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.isfile('./../data/tmp/9_model.pkl'):\n",
    "    model = check_model(None, data={\n",
    "        'X': m_diff_q1_q2[ix_train, :],\n",
    "        'y': df.loc[ix_train]['is_duplicate']\n",
    "    }, do_scaling=False)\n",
    "    print (model.best_score_)\n",
    "    print (model.best_params_)\n",
    "    with open('./../data/tmp/9_model.pkl', 'wb') as f:\n",
    "        pickle.dump(model, f)\n",
    "else:\n",
    "    with open('./../data/tmp/9_model.pkl', 'rb') as f:\n",
    "        model = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "Fitting 5 folds for each of 90 candidates, totalling 450 fits\n",
    "[Parallel(n_jobs=-1)]: Done  18 tasks      | elapsed:  2.5min\n",
    "[Parallel(n_jobs=-1)]: Done 168 tasks      | elapsed: 13.9min\n",
    "[Parallel(n_jobs=-1)]: Done 418 tasks      | elapsed: 34.0min\n",
    "[Parallel(n_jobs=-1)]: Done 450 out of 450 | elapsed: 36.1min finished\n",
    "0.630802146974\n",
    "{'en__l1_ratio': 0.01, 'en__alpha': 0.001}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred = model.predict_proba(m_diff_q1_q2[ix_test, :])[:, 1]\n",
    "y_test_pred_fixed = link_function(y_test_pred)\n",
    "\n",
    "if not os.path.isfile('./../data/tmp/9_pred_fixed.csv'):\n",
    "    pd.DataFrame.from_records(\n",
    "        zip(df.loc[ix_test]['test_id'].values, \n",
    "            y_test_pred_fixed), \n",
    "        columns=['test_id', 'is_duplicate']).to_csv('./../data/tmp/9_pred_fixed.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = plt.subplot2grid((1, 2), (0, 0))\n",
    "ax2 = plt.subplot2grid((1, 2), (0, 1))\n",
    "sns.distplot(y_test_pred, ax=ax1)\n",
    "ax1.set_title('Model prediction')\n",
    "sns.distplot(y_test_pred_fixed, ax=ax2)\n",
    "ax2.set_title('Model prediction fixed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- результат: **0.46146**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "data={\n",
    "    'X_train': m_diff_q1_q2[ix_train, :],\n",
    "    'y_train': df.loc[ix_train]['is_duplicate'],\n",
    "    'X_test': m_diff_q1_q2[ix_test, :],\n",
    "    'y_train_pred': np.zeros(ix_train.shape[0]),\n",
    "    'y_test_pred': []\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.isfile('./../data/tmp/10_model_pred.pkl'):\n",
    "    n_splits = 10\n",
    "    folder = StratifiedKFold(n_splits=n_splits, shuffle=True)\n",
    "    for ix_first, ix_second in tqdm_notebook(folder.split(np.zeros(data['y_train'].shape[0]), data['y_train']), \n",
    "                                             total=n_splits):\n",
    "        # {'en__l1_ratio': 0.01, 'en__alpha': 0.001}\n",
    "        model = SGDClassifier(\n",
    "            loss='log', \n",
    "            penalty='elasticnet', \n",
    "            fit_intercept=True, \n",
    "            n_iter=100, \n",
    "            shuffle=True, \n",
    "            n_jobs=-1,\n",
    "            l1_ratio=0.01,\n",
    "            alpha=0.001,\n",
    "            class_weight=None)\n",
    "        model = model.fit(data['X_train'][ix_first, :], data['y_train'][ix_first])\n",
    "        data['y_train_pred'][ix_second] = model.predict_proba(data['X_train'][ix_second, :])[:, 1]\n",
    "        data['y_test_pred'].append(model.predict_proba(data['X_test'])[:, 1])\n",
    "        \n",
    "    data['y_test_pred'] = np.array(data['y_test_pred']).T.mean(axis=1)\n",
    "    with open('./../data/tmp/10_model_pred.pkl', 'wb') as f:\n",
    "        pickle.dump({\n",
    "            'model': model,\n",
    "            'y_train_pred': data['y_train_pred'],\n",
    "            'y_test_pred': data['y_test_pred']\n",
    "        }, f)\n",
    "else:\n",
    "    with open('./../data/tmp/10_model_pred.pkl', 'rb') as f:\n",
    "        tmp = pickle.load(f)\n",
    "        model = tmp['model']\n",
    "        data['y_train_pred'] = tmp['y_train_pred']\n",
    "        data['y_test_pred'] = tmp['y_test_pred']\n",
    "        del(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = plt.subplot2grid((1, 2), (0, 0))\n",
    "ax2 = plt.subplot2grid((1, 2), (0, 1))\n",
    "\n",
    "sns.distplot(data['y_train_pred'], label='train', ax=ax1)\n",
    "sns.distplot(data['y_test_pred'], label='test', ax=ax1)\n",
    "ax1.axvline(np.mean(data['y_train_pred']), color='b', alpha=1, linestyle='--', label='mean(train)')\n",
    "ax1.axvline(np.mean(data['y_test_pred']), color='g', alpha=1, linestyle='--', label='mean(test)')\n",
    "ax1.legend(loc='upper right', prop={'size': 18})\n",
    "ax1.set_title('Train/test OOF predictions')\n",
    "\n",
    "sns.distplot(data['y_train_pred'][:ix_train.shape[0]//2], label='train first part', ax=ax2)\n",
    "sns.distplot(data['y_train_pred'][ix_train.shape[0]//2:], label='train second part', ax=ax2)\n",
    "ax2.legend(loc='upper right', prop={'size': 18})\n",
    "ax2.set_title('Train/train OOF predictions')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['m_diff_q1_q2_tf_oof'] = np.zeros(df.shape[0])\n",
    "df.loc[ix_train, 'm_diff_q1_q2_tf_oof'] = data['y_train_pred']\n",
    "df.loc[ix_test, 'm_diff_q1_q2_tf_oof'] = data['y_test_pred']\n",
    "del(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VbiqVu0DRgegQ8wgUivZGhdPNxoMFnDuiO2tkDrqukxBp490fD2MFJg9LJTzMGmoxFQqvUD0ChcIPNh4soNLl4bSBxu6LmqYxvHss6QXlVCjzkKKDoRSBQuEHX+/JIybcysjecTVhQ1Ki8eiwP1/Ngld0LJQiUCh8xO3R+XZfPqcPTMJmPfYI9YqPIMJuYW9OWQilUyh8RykChcJHfj5STKHDyVlDkuuEWzSNwd2i2ZtbriaXKToUShEoFD7y9R5jEtlpAxIbnBucHE25043MKg2BZAqFfyhFoFD4gK7rfLM3l3H9EogJbzjoblByFJoGGw+EZA9yhcIvlCJQKHxgf345GYUVnDW4W6PnI+1W+sRH8P2BgiBLplD4j1IECoWXVOqwPC0HgFH9EiioctPYfklDUqLZk1NGVkllkCVUKPxDKQKFwkvKnW6W78iiR1w4vxwqYnVaNk5PQ00wNCUGgFW7c4MtokLhF0oRKBResj+vjMziSkZ0j202XrfoMIakRLMiLTtIkikUrUMpAoXCS5Ztz8KqaZzYK67FuJOGJrMts4RDhY4gSKZQtA6lCBQKL6hwuvkqLYfjUqOJ8mINoUnHGfuFr5Q5gRZNoWg1ShEoFF6wancuJZUuRveJ9yp+amw4o3rFsSJNKQJF+0cpAoXCCz78OZNe8RH0T4z0+pppw1LZk1vG3ly15ISifaMUgULRAntyy9hyuJjzj++OpmleXzdVJGPRYNlO5TRWtG+UIlAomkHXdZ5evZeYcCvTR3T36dqkqDAmDu7GBz9lUlrpCpCECkXrCejGNEIIO/AqMAAIB/4G7AAWATqwDbhZSukJpBwKhb98tSuX79ML+b+zBxMfaff6Ok3TKKhyM29cH9bsyWPRD4e4fHxfouxWwr3vVCgUQSHQPYIrgDwp5ZnAucBzwFPAvWaYBswIsAwKhV+UV7l5es1ehqZEM2tUL5+udbg8rE7LJiOvnCHJ0bzzwyGWbztKudq0RtEOCbQieA+4z/ytAS5gLPC1GbYMmBpgGRQKv3h1YzrZpVXcM2UINov/zfgzBiVR4fKwOaOwDaVTKNqOgCoCKWWplLJECBELvA/cC2hSyup5+SWAd+PxFIogciC/nLd+OMQFI1IZ1bt1VbRnfASDk6PYeLAQR5XqESjaHwF3Fgsh+gKrgTeklIuB2v6AWEA1kxTtigqPzmNf7SHMZuGq0/pTUOVucoE5bzl9YBIOp5tPtmW2naAKRRsRUEUghOgOrADukVK+agZvEUJMMn+fB3wbSBkUCl9ZmZbDj+mFTBiYyNb0QlanZTe5wJy39E6IZGC3KN7bfERtbq9odwS6R/AnIBG4TwixRgixBsM89JAQYj0QhmEyUijaBaWVLl5Yu5+UmDDG9klo07TPGJREkcPJBz+rXoGifRHQ4aNSytuB2xs5dVYg81Uo/EHXdf7yxS5ySyu5fFwfLK1wEDdGn4RIRveJ5/VNh5g1sicR9pbXLFIogoGaUKZQmPxv82FW787lugkD6JPg/VISvnDFyX3JK6vizR8OBSR9hcIflCJQKIBfjhTz7Df7mTSkG7NP8m3OgC+M7B3P9GEpvLwhne1HSwKWj0LhC0oRKLo8Dqeb+5el0T0mjPunC5/WE/KHu6cMITk6jPs/T8OhHMeKdoBSBIouz7Nf7+NwYQX3nyuIjQio2wxN03BbLNw1dQgZBQ4eX7WXgio3la0YmqpQtJbA1nqFoh1TqcO3e/N4/6dMZo3uxaDusa2eL9ASDpeH9buNPQpO7p/Ip9uOEmHTuOHMQYR7seGNQhEIVI9A0WU5XOTg4eVpdIsOY1BSZJvMF/CFiUOSSI0J4/Pt2RSUVwUlT4WiMZQiUHRJ3B6dx77YRYXTw4wTe2C3Bv9RsFksXHRiDyrdHp76ag+6ruxDitCgFIGiS/LS+oNsOVTE9GEpdI8ND5kcKTHhTB6azMYDBfxv8+GQyaHo2igfgaLLsWp3Lq9uSOfcEamMbOWCcm3BuL7xOJxunv16H8O7x3KSl/siKxRtheoRKLoUWw4Vcd9nOzmxZxw3nzUo1OIAxkii308dSu+ESP746U5ySytDLZKii6EUgaLLsC+vjLs+3E7PuAieuvh4wm3tZ5ROdLiNxy8aQXmVi99/tEMtTKcIKkoRKLoE6QUObn7vF8JsFp6dfSIJPmw7GQw0TSMpLoK7zzmOHUdL+NPnaeRVutT8AkVQUIpA0enZX+jgxnd/wun28MiMEURG2gM+X8BXqre2rHK6Ofu4ZL7dk8eDn+5UW1sqgoJSBIpOTU5pJbe+9zOlFS5mj+7FgZyyoM8X8JXx/RI4qU88Gw4UsGz70VCLo+gCKEWg6LSUVrq4/YNtFDucXDqmd0iHifqCpmlMEykM7BbFs2v2sfFgQahFUnRylCJQdEqcbg93f7yDfXnl3Hf+MHrGR4RaJJ+wWDQuHtmDfomR3PPxDmRWaahFUnRilCJQdDpyy6q45f1f2JReyH3TjmNcv8RQi+QX4TYrf71wBDHhNm545ye+258fapEUnRSlCBSdiq2Hirjyjc1sP1rCX84XXHB891CL1Cq6x0XwzJwT6ZUQwZ1Lt/HG5sNqtVJFm6MUgaJTUOXysPCb/dz47k/GENG5Izl1cHK7Gx3kKw6Xh58PFTHjhB6Gz2D1Xu7/ZAelVa5Qi6boRChFoOjw7Mou5erFW3hzUwYn9ozj0jG9OJDb/kcH+UKYzcKcUb1qRhM9slxSWqmUgaJtUGsNKTosLo/OG5syeGndQeIibPzlV8NxdOKXo8WiMX1YComRdlbvzmXeoh+4++whTBqaHGrRFB0c1SNQdEg2Hizgqjc38/zaA0waksw7V4/j1IFJoRYr4GiaxikDEnl23kgSIu3838c7+L+PtpNdotYnUviP6hEoOgy6rrPlcBGvbMjg+4MFdI8L597zBBOHJKNDh/YF+MqwHnEsnDuSD7Ye4Y3vM5j72g/MP7Ufc0b3Ik7tdKbwEaUIFO2ewnInq/fk8vG2o2zLLCE+wsbZQ5MZ2y8et7k0A8BpQ1NCLGnwMLa8zCUlOoxrTunHF2nZ/Pvb/Sz+4RCXntSLOaN7tbv1lBTtF6UIFO2SQoeTr/fk8qXMZVN6AW4dY3LVlCGcPjSZ9XvzQi1iuyExys4lJ/UivcDBntxyXlx3kEXfZ3DRCT2YN7oXA7pFhVpERTtHKQJFu6GgvIpv9+azclcOm9ILcXt0esVHMGdMb84amszg5Gg0TetSJiBv0TSN/klRXHP6QPbnlrFky2GW/pzJe1uP0C8xkrOGJjNlaDIjusegaVqoxVW0M5QiUIQEj66TXuBgV3YpaVmlbEovJC3bWEahd3wEl4/twymDksjIK0PTNDLyysnIKwe6lgnIVxwuD/tzShnTJ57jUqJJyyplV04pb23K4I3vM0iNCWNM3wSOS4lmRI9Yju8RS4Rd+RS6OkoRKAJKaaWLw4UVHC5ycKiwgkNFDvbmlrM7pxSH0wOAzaIxvEcsV5/aj3H9Exmacqzlfyi/PMQl6LjEhNsY1y/B+AxIYsP+fNbvz+eH9EKW7zT8KjaLxvDuMQxNiWFAtygGJUUxoFsUqTFhqufQhQiJIhBCWIDngVFAJXCdlHJPKGRRNI6u61S6PFQ4PZQ73ThqfcqrPHWOq8MqnG7KnW7Kq9xklVRyuKiCQoezTrpxETb6JkYybXh3hqREMyQlmt6JUazfkwvA4fxyDuerln9bY7dbsWlw5qAkzhyURHmViyNFlVitFrZlFrNS5lBSaw5GpN1CYlQYCZF2EiJtJEbaiY+0m8fGJzHSTkyEjegwK1F2K9FhVmxWNSK9IxKqHsFMIEJKeZoQ4lTgSWBGW2dSUuFi2c5sXB6j5anXsi1X/9T1xg3OjcWtH78pU3Xda/Umwhsm0mLc+nnqOm4dPB4dt67jMX97zN9uXa85dtecwzyv4/boOD268QKvclPh8lBedezl7suEXLtFIyLMSoTNQlSYjaRoO6cPTqJnXAQ9441PamwEPx6ou3Bael45vZOivc9I0SZEhdkYkmLjtKEprN+dg67rlFe5yS2rIresipgIO4UOJ8UOJzllVezNLaeowkmF2YtrCrtVq1EKEXYrdqsFm0XDbtWwWTRs5rHNomG1aGhoWC2Gj8OqGd8WDSyaZn6M35pGg/jH4oGG2Xup1Ymp/qnVCdMahDWMrzUS1kh8M1CrE9bs3+Mz4TYL5w3vTlSAhwRrTb0IA4kQ4ingeynl2+bxYSll72YuyQEOBkU4hUKh6Dz0B1rsWoeqRxAHFNU6dgshbFLKptYHUDYChUKhCBChMugVA7G15WhGCSgUCoUigIRKEXwHnA9g+gh+CZEcCoVC0eUJlWloKXCOEGIdhq/lmhDJoVAoFF2ekDiLFQqFQtF+UIN+FQqFooujFIFCoVB0cbr0EhMtzXAWQtwBXGoefi6lfEgIoQGHgN1m+Hop5R+DKLZPeFHGm4GrMeaqPSGlfFcIEQm8CaQCJcB8KWVOsGX3Fj/L2KnuY604nwEfSSlf6Gz3sVac2mXsUPcRvKqvC4EzMO4ZGJNt7cBiIBI4AlwjpWyz9Ve6eo+gZoYz8AeMGc4ACCEGAZcDE4BTgWlCiJHAYGCzlHKS+WnXlY7my5gM3IRRxinAk+aDdRPwi5TyTOB14N6gS+0b/pSx09zHWvwNSKx13GnuYy3ql7Gj3UdouZxjgem1ylQE3A8sNu/lFuDGthSoqyuCM4DlAFLKDcC4WucygHOllG4ppY6hkSswblJvIcRqIcTnQggRbKF9pMkySilzgdFSSifQA6gwy1pzDbAMmBpUiX3HnzJ2mvsIIISYA3g4dt/qXEMHv4/QZBk72n2EZspp9haGAi8JIb4TQlxb/xoCcC+7uiJodIYzgJTSKaXMFUJoQogngC1Syl1AJvColHIy8AhG17s902QZAaSULiHELcAGjpWl9jUlQHwwBG0F/pSx09xHIcQJwGUYrcamrunQ97GZMna0+wjN19do4J/AFcC5wG9NS0RA72VXVwTNznAWQkQAb5lxfmsG/wB8BCClXAv0Mk0N7ZUWZ3FLKZ8DegIThRCT610TCxQGQ9BW4E8ZO9N9vAroDazC8IXcKYQ4l851H5sqY0e7j9B8OcuBhVLKcillCUZ5RxHge9kh5hF4PB7drbalUigUCp+w2625tONF53zC7dYpLFQblCgUCoUvpKTEerVqc1c3DSkUrcKj68isUv63+TA7jpa0fIFC0Q7pED0ChaK9ctsH29h4oACAYd1jeHbeKKLsVsLbu5VaoaiF6hEoFH5yqNDBxgMFjOkbz8n9EkjLKuXjrUcod7pDLZpC4RNKESgUfrJql7HP8in9ExnVOw6A3TmloRRJofALZRpSKPxk1e5chqZGkxBpR9d1EqPsyOyyUIulUPiM6hEoFH5wtLiC7UdLOGNwMmBsZC5SY0gvKKe0Um22p+hYKEWgUPjBmj15AJw5uFtN2NCUaDw6bDyQHyqxFAq/UKYhhcIPVu3OZXByFH0SI9mdZQwb7R0fQXSYlXX78plzYs+gy+RyOSkrK6ay0oHHoxzWnRGLxUp4eCTR0XHYbPY2S1cpAoXCR4ornPx0uIirT+5bJ1zTNI5LiWbTwQIqXR7CbcHrcLtcTvLzs4iKiiUpqQdWqxVNU2NYOxO6ruN2u6moKCM/P4ukpO5tpgyUaUih8JGNBwvx6DBhYBIANrcDzKVajkuNocLp4fuDBUGVqaysmKioWGJi4rHZbEoJdEI0TcNmsxETE09UVCxlZcVtlrZSBAqFj6zbn09chI0TesZhKzvKlRvPZfjRDwDonxRFVJiVr00fQrCorHQQEREd1DwVoSMiIprKSkebpacUgULhAx5dZ/2BAk7pn4jVohG/803C3OWclPFfNN2F1aJxyoBEvtmbh9sTvIUSPR43Vqs1aPkpQovVam1TP5BSBAqFD+zKLiWvrIrTByaBu5L4tP9RGpZKbGUmg3JXATBhUDcKHE5+PtJ2XXdvUOagrkNb32ulCBQKL6nU4SvT5DO8dxyutI+xOXL5+rh7KYzsx8hDb4GuM75/Anarxpo9uSGWWKHwDqUIFAovKXe6Wbkzmx6x4WxNL8Ty48s4Ewbou3Y9AAAgAElEQVTRc9BIiofOIbV0B5P1DfS1FTChfxxf7crF0wH2+1AolCJQKLwkr6yKw0UVDEmJJqVkO91LtuMYNpvSHV+QXmrBaYkkaes/ccmVnD8kiqySSn4JsnmoK3DLLTcwZ86FAUv/4Ycf5IwzxrUcsROh5hEoFF6yttos1D2GYZkf4bREUjHkAsj9Ao/FztG4kfQu3ESu28FpA6MJs1r4ZEc2/VJiQro0daVOu1oRtbX/xfz51+JwVLSdQAqlCBQKb/lmTy7J0WGkRlkZlLuKA90mEmc/NmQzN0bQt3Ajttyd5KZOYVC3KL5My2ZYajRThncnPCw0o3rKnW5Wp2WHJO/GmDwstVX/xfjxp7ahNApQpiGFwitySyvZdqSYYd1j6F24kQhXEXtTptWJUxLei0prDJF52wEY3iOGsio36QVtN95boQgEShEoFF6wancuOoZZaEjOCiptsWQknlY3kqaRGyOIKNyFxVXO4ORowqwaOzLVFpZtSX0fQVVVFQsXPsncuTOYPPk0Zs26gCef/DvFxS37Z9LSdnLHHTdzzjkTmTnzPN544zX0eg7+pnwG9cMffvhBLrlkJtu2/cyCBVdy9tmnc9lls/nww/dbUdrgoExDCoUXrJQ59E+KonukzoC8NexNPgePpeE6L7nRgt5FPxKX/T32iPMZ1j2WHVkllFW5SAyRaaiz8/TTj7Ny5XLmzv01vXv3Zt++vSxZ8i6HDqXz9NP/avK6ffv2cuutNxAbG8fVVy/A6XTy9ttvUlXl9FuWoqIi7rrrVk499XTOP/9XfP31ap544jFKSkq48spr/E430ChFoFC0wPbMYrYeLuaWM3pxtnM1Ye5ybIPPYnxSOZGWsDpxiyL74rZFkXDkaxh0Pif1iefnI8V8lZbD/HF9QlSCzs2KFcu44IKLuPHGm2vCIiOj2LhxPeXl5URFRTV63auvvgho/Pvfr9C9ew8AJk+eyjXXXOa3LKWlJcyd+2tuv/0uAGbOnMPtt9/EokUvM2PGbOLi4vxOO5Ao05BC0QIvb0gnPsLGJcOjiN3xFlXWaDKzsijdvhyLp6puZM2CI2kE8UfXY/VU0is+gh5x4Xzyy9EGJgdF25CS0p1Vq1by+eefUFJimOGuv/4mXn759SaVgMfjYePGDZx22uk1SgCgf/8BnHxy65zRV155dc1vq9XK3LmXUllZyQ8/fN+qdAOJUgQKRTOkZZWwdl8+l43tQ0zFYZLLdpEdezxoTT86jm4nYHU76J/3LQBj+sRzML+cLYeLgiV2l+L3v/8DHo/OI488xK9+NZWbb76ed955i9LSpvePLioqwuEop3fvhr20fv0G+C1LXFw8SUnd6oT16dMPgKNHj/idbqBRikChaIZXNqQTG25j3km9iNr8Ih7NRkZC8y3GyoQhVEb2YETmEgBG9IglJtzKu1va74ugIzNu3MksWfIpDz74MFOmTOPgwQP8859Pc9VVl1BQ0Phy4NVr9VRWNpyP4G3PrbFF3+z2htb26ngWS/t93bZfyRSKELM7p5Q1e/K4dEwv4kt3E7b3Cw4njMNpa2G5Z81C7oAL6V30Awnl+7FbLfzqhJ6s2pXL3ly1uX1bUlVVxfbt2ygtLWHq1Oncf/9f+fjjL/jtb28nOzuLr776otHr4uPjiY6O5tChjAbnjhw5VOe4+gVeVVXXDJiX13Cp8fz8fMrLy+uEZWQYeVT3DNojShEoFI1QqcOL6w4SZbdy7gk9CVv/D/SwaA4lnOLV9Xn9L8Ct2Wp6BbNP6kWk3corG9IDKXaXo7i4iN/85hreeOO1mjCLxcLw4SPM342P1NI0jYkTJ7Nx43r27dtbE56ZeYT167+rE7fa1LN7966asOzsLLZt+7lBurqu88EH79Ycu1wu3ntvMTExMYwbd7IfJQwOAR01JISwA68CA4Bw4G/ADmARoAPbgJullJ5AyqFQ+EpaVglf78ljwsBEjmz9grEHV1A65iZcxZFeXe8KT2Rf8hSOy/qM7wfcTHxkKvNO6sV/v8/gutP6Maib2kSmLUhOTmHatHNZuvR9KioqOOGEkRQVFfHBB++SlNSNs88+p8lrr7vuN6xfv5Zbb72BefMuw2q18v777xAVFVWn9T9lyjTefHMRDz74J+bNu4yqqkqWLHmXlJRUMjIaKvZFi17m6NFMBg4cxKpVX/LLLz/zhz/cR0RERED+g7Yg0MNHrwDypJRXCiGSgK3m514p5RohxAvADGBpgOVQKHxi8Q+HsFs1JvbUOXvb/RRE9qdqxK9hwzKv09jRczZDc75gSPZytBN+wwUje/LOlsP8+7uD/OlcEbT1h6LsViYPSw18Rl4SZW/b+RR33/1nevXqw5dffsGXX64gMjKCsWNP5oYbfktCQkKT13Xv3oPnn3+F559fyOLFr2O3h3HhhTMB6vQwhgwZyl/+8iiLFr3M888vJDW1O1dccTUVFRU8//zCBuk+9dRzPPHEo3z++acMGDCQhx/+B2edNblNy9zWBFoRvAdUT6vTABcwFvjaDFsGTEMpAkU7Ir3AwZpdOZzcN54L9j1EuKuEz094lol273oD1RyNG01OtOCkjEUcrLiKzQdLGN07njW7cxmaHMXccX2Dsv5QuEbI1jkKBM8991Kd4/DwCBYsuJEFC270Oa1+/frz2GNPNQivPScBjPkFkydPbRDvssuubBA2cuRoXn/9HZ9lCSUB9RFIKUullCVCiFgMhXAvoEkpq93yJUB8IGVQKHzltY3p2K0WbrV/RN/CjawbdCf50UN9SiM52sr4bg4KR91IXOURBmx/jvFJ5Vx5krFpzdp9+QGSXqHwnYA7i4UQfYHVwBtSysVAbX9ALFAYaBkUCm85VOhg2Y4s7hxwgAmH/8PulOns7HGxz+nYPRWUbl9OZnYOeVFDiN76Hyp/WkKPsCrG9U1gZ1Yp6fnlLSekUASBgCoCIUR3YAVwj5TyVTN4ixBikvn7PODbQMqgUPjCf7/PYIAlm+uyHsYRN5jSk+9kfDeHuZyEy6809yWfjeZx0j/fqOon90/EbtVYpEYQKdoJge4R/AlIBO4TQqwRQqzBMA89JIRYD4RxzIegUISUQ4UOvtu+m7ein0bTXWxLPIcSuYbS7csbX07CSxxh3SjtcSo9i7cSVbCTqDArpw5IZO3ePDYcUCaizsKf//wga9f+EGox/CKgzmIp5e3A7Y2cOiuQ+SoU/rDk6/W8Z3+AVHcBRcOvoKKi6REnvlLcdyph2T/Tb+sTWE58g1MHJLI/r5zHv9rD/+aPI9ympvQoQoeqfYouT6UOu3Zu4vaMW0mxlVNy3vNUJAxp0zx0WwR7UqYRVbyHEw8vxmaxcOukwWQUVvDf75WJSBFalCJQdHmcObsZsfpqXFh5/4QX2WsdgtvT9iuF5sUICnucwbj0l4hzpHNS3wSmD0th0fcZHFSOY0UIUYpA0aWxlBwm9ZNf4/boLOzxOBVxbdsTqE/GyN/hsoRz3vY7sFYWcM2EAditFh7+cjf5lS4q1UrVihCgFIGiyxJZuo+ED+fiqSjm9/Y/c/H4IYxPKic5KnBvY2dkCl+MeJKYyix6LLua7QeOcsagJLZkFPGvNfsodzZc0VKhCDRKESi6JJaSI0QtvQxXSTbzK+/mlGQnlWlfULp9OXbdv2Gi3pAcbaXvwOM4OPZeonJ/Zs7eu7n+eI2BSeF8KXMoKPdvZJJC0RqUIlB0OSxFB0n4cC66o4Arqv5AUmpvjo8JzvLQ1RPNDheUUzjwV8RnrafPt3dx+/g4Kt0enlm1V+1kpgg6ShEouhS2zE0kvn8hWmURf419kO0M4fahjW9eEmjKepzKwcQJ9Cz+iVOOvsWkId1Yvz+fT7dnhUQeRddFKQJFlyF898ckfHgJnvB4vhj3XxYd6c1vhxSRGhE6u/zBpIkcjR1JT7mIBdbPGNk7jidX7yWzuOHOWYrAUVCQj8Ph8OvaW265gTlzLmxjiYJLoFcfVShCTgRlhG15mbB1T+DpMZq8s/7OQ0sKOa6bncv6l+AM5W4Ymsau1PMIj01iwv6FPD0qgguzx/DQcsnzc0di0Vq/TnUEZVicTe/fG2w89hgqaD/7Maxf/x0PPXQvr732FpGRvq0w21lQikDRudF1wjf8g7DNL+NMHkHp0Fn8ZcUBcsqieXpaKs5inZDviqRZ2D/2PvKrbAz+6e+8PPAmLtt1Bq9tOsSs0b1avW+BxVmKZ9fKtpO3lViOOwfs7UcR7NhhbHXZlVGmIUXnxeMm5us/Erb5Zap6jKNixOUsPxrHZ5kxXJiczYA4DXm0OCCTx3wlOTacotP+iGPQdCak/5sX419n0Xd72LwnQw0pVQQc1SNQdE7clcStvI3wvZ9RNfpqKuMF+8rsPLoziYGR5VyUmh1qCetg91RQuvMr8rqfiSe/iOmFX/CCLYvH19/FyIG9gc6zsUxrmTPnQk455TRGjhzNG28s4siRQ6Smdmfu3F8ze/a8OnF/+mkLr776H3bs+AWA4cOP59prb2D06DEAPPzwgyxb9ikAc+dexOjRYxpsfFObTZs28sorL7Jnzy6Skrpx1VXXNIhzyy03cPRoJu+//0mz4bfccgNWq425cy/lhRf+yZEjR+jfvz/z5y9g0qQp/v9BfqB6BIpOhzUvjcT3LiR872eUnn4/zpNvJafSyk0/pGK1wC1907EFYYtIv9AsHEiezO6U6Uy0/MSj5Q+wdN1PoZaq3bFhwzqeeeYJJk+ewq233klERCRPP/0469evrYmzdu3X3HrrjWRlHWX+/AXMn7+ArKyj3H77Taxda2ySOGPGLCZONLaRvO22O5k//9om89y0aSO///1tlJaWcv31NzFlyjSeeeYJpNzpdzkOHNjHfffdw+jRY7jpplvRNAv33nsPK1Ys9ztNf1A9AkXnweMicut/iN74D/TwOIrOf42qgeegl2Vy8+ZUCpwW/j02C0u5M9SStkhm/BgqbbEMO/oJSTuuZ0Pcc5w6/vRQi9VuyM7O4rXXFjNkiLFz3MSJk5g58zxWrFjOaaedgcvl4qmnHic5OYVXXnmd6OgYAGbMmM1VV13Ck0/+nVNPPZ0TThjJ4MFD+Oab1Zx55iR69uzVZJ4vvPAc3bol8+KLr9akN378Kdx222+Ij/dvpdq8vFxuu+1O5s27DICLLprJ/Pm/5vnnFzJ16jQsluC01VWPQNEpsGX/TNJ75xOz/mHcfU/DMed/2PqMxF5xlDs/P8SuEjtPjM5leFzHmbmbHz2U3acvJNrqYtrGK8j55nlQk80AY6/haiUA0K1bMklJSeTn5wGwa1ca2dlZzJ49r+alDRAbG8usWfPIyckmLW2H1/kVFOQj5U6mTp1eJ70xY8YxeLBv25jWJiYmhosvnltzHB4ewcyZs8nNzWlVT8NXlCJQdGyqyohe+xcS3v8VWlkWjhGX4xhwLp6MH6lIW8mfP9jM2nQHfxiWz7hER+hHCPlIXL8TKJm1mC3WExnxyyPo784jqjiNCIIzE7q9kpCQ2CDMbg/D7TYc65mZRwBDYdRnwIABABw9mul1ftVxe/fu0+Bc//4DvE6nPr169cFut9cJ69OnHwCZmd7L11qUIlB0THSdsH1fkPT2FKJ+eomKEZfhmLcEV+qJoGnoOvx9ZwKfZcawYFQUwnqk3YwQ8gW7p4KI7K3EjZnH85bLiM/ZBItnYdnXfoaDhgKthfkVzS3T4THrQP0XsDf5VVZWNpKed82LxuI1JkN1PKs1eK9npQgUHY6orPUkLZ1J/LIFaLYwHBe9jOf0O7CFRwDg1uEpmcC7GXFM75bLgtHtZ8y6PzjdOrmFRQzuP5C7bH/kiDOW6GW3EL3sRiyF+0MtXrukRw/D1n/w4IEG59LTDwKQmtrdp/Q0TePQoYabCB05crjOsdVqxelsaIKsNlvVJjPzSAOlVZ1Hdc8gGChFoOgw2I5uJu7T+US/PxctV1IxdCZlJy7AVZyLZ9dKNHcVRxxWrvs+lUUH4pjdp4TLemS22HrsKERaPcwZ4OLFuNtY6JqFtu8rEhdPJmb1/4E7cCumdkSGDRtOt27JLF36PmVlx2ZVl5WVsnTpe3TrlowQwwHjxQ3Nt+wTEhIYNeokVqxYVueFvm3bz+zalVYnblJSNwoKCsjNzakJS0vbyaFDGQ3Szc/PY9WqY707h8PB0qVL6NOnH4MHB3ZvjNqoUUOKdo3myCN890dEyCXYs3/CE55A1fibqQxPAVtETTxdh49lOQ9+1xOPDn89IY9zupeyq5Ot32bR4KIehZRPuI1frZjOAvf7zEtbgqX3TKyFoEel4LHHQidRfv5is9n43e9+zwMP/IkFC67iwgtnAPDJJx+Rl5fLX//695oROQkJxoif//3vDU49dQJnnNH4luq33HIHN998HTfeeA0XXzyXigoH7767uOb6aqZOnc7Klcu5667buPji2eTn57NkyTv06dMPl6vuiDWbzcYjjzyElGkkJ6fw2Wcfk5OTxeOPP9PWf0mzKEWgaH+4Kgg78CXR8h2sB79B0924uwkqT7sLl5iBLSwMdq2piZ5dYeXvaQmsOJrPyPgqHjw+j95Rrg7nGPaFiX0s9BpXxL2//JonimbzohYFTgeWwv1otkg8USno4fGgWfDYY4xlHdoJHntMy5HagMmTpxIbG8eiRS/z2mv/wWazMWLECfzxj/cxatRJNfGmTJnOmjWr+PzzT9iy5ccmFcGwYcN57rmXeOGF53jttZeIjY3j2mtvIC1tJz//vLUm3umnn8mdd97Du+/+j4ULn6Rv337cddcf2br1R9atW1snzeTkFG677S7+9a9nyMvLRYjhPPPM8zUT3oKFUgSK9oHbif3IRsL3fEz4nk+xVBXjiUrG2ed0nN3H4InpYcQ7sA5t8AQAyl0ai9NjeWlvHC6Pxo1jojmt8hdKi0EWw6DU2BAWKLB4dJ3CwgLu6FPA5xEpFOhR7PT0oE+4g1h3EdbidHSLHT0yiYrweLCldppeQv0Zu82Fjxt3MuPGndxserGxsTzzzPNe5T18+PEsXPjvFuPNmjWXWbPm1gk7++yp3HnnPQ3iTpw4iYkTJ3mVf6BQikARGjxurPkSe+b3RBz+Flv6WjRnGbotAtfAKVQOPR9Lr9FU7fm27mU67Mip4su9cSw+GEt+lZVJqeXcOqSAXsedxO4fQlSeEGHV4MKUHCJsLmxWOFARjUWLJtlWQRKFhJVlYynLQreGodtj0cOi0e1RYAnrNIpB0XqUIlAEHncltvxd2HJ+wZazzfjk7UBzGWvue6K74+w2HHfycFyJQ8EaBuUlhOtudB0yym1syItgQ14Em/LDKXRmAwmcnOTgukFFjEqo7NRmIG+waTp9olxUWqMoLKuk0BVOtqcHdtwkWspI0B2EVxZiqTAdnZoV3RYOtkg8tih0WyTYwkFT40e6IiFRBEIIC/A8MAqoBK6TUu4JhSyKNsDjwlKWjaUsE0tpJtZS49tSlom1cB+2fInmMUa16PZoPMnDcA2bhTtlOJ4eo7HGJFO121j7xemB/SV2ZLGdzQfz+W5/LzIrjGqaEu5iQrKDM4b3JerwOpLsLqgAedQwA3X1Zdl0dMKtEG+tIt4KLl3DiY1SVww57jjQIVqrJM5SQaRWRbirCquzACvVo2A0dFtEzQeLHSx2dIsNLDbQrKoX0UkJVY9gJhAhpTxNCHEq8CQwI0SydE08LqNF7nKguRxoroqa75owpwOtqgStqgRLVTFaZbHxu7LQ+F1ZhKWyGK0iH02v2ybXbRHo0am4YnpROuJyyhMEjvhBFB5Np9Bpp6DKQv5BKwV7MiiO1MnO7sa+Mjv7Su24dONlExfuYExcJZf3LyLFk0uPsCo0DcYIweZsNVyyJWyaTrTdTaTmpFd8LIUl5TjcVvL0OKrcGjqgAWE4ibFUEaVVEeGuIsxdgrWi4fadOhq6ZjMUg2YFixXdYgXNimaxgfkbzQKaBV2zmIrDUhNWV5GYv7uocmluldNgEypFcAawHEBKuUEIMS4QmVgL9hK94THwOGut0VJr8oYZptHYuXo/mrmeRq8/FuZ06xwudODR9Zq8dL12vtST41haWoP8jTArbiy6Bwu1Prq73nH98x4sGHGsPhpTnNgo06Io16Ip1aIp1WIo1XpSpg0hzxZHpt6NI54kDnuSyHAnklsVibNUgzrDNz1Awyn6MWFlJFjD6Bvl4pR+DmLcRfSNqGDihNPZt/lnBqXGsi+746wR1B6xahBtdRNtdRNud1Ph9BgKNyyaqio3Ve5I8jyRuHQNl66h6R5suLBrbmzU+uhubB43NlxYqcSqGXWpfl32FZ26CkL38ly1MtHrn9caP3fs+FiaLR23Fk2DcJuf/VXNgiemp2EuDSBac1OxA4UQ4mVgiZRymXmcDgySUjbVzMsBDgZLPoWio7Ft2/YTe/UaoLpJXYgjRw7YTjjh+F9aiNYfSGkprWZ7BEKIh6WUfxZCzJBSfuSLkC1QDNQe22dpRgmAFwVRKLwlgPU6ZDidzgMulzu3s8yiVjSPrus4nc5koE2sKS2ZhuYJIY4Atwoh6izMIaVsjYHrO+BC4F3TR9CSVlMo2pJA1euQoWkUuN0uu81mb/+bLShajdvtsmsaDR05ftLSWLHLgSQgHOhZ79MalgIVQoh1wNPAHa1MT6HwhUDV65Ch6/qy8vLSzjuDTlGH8vLSWF3Xl7VVel75CIQQ44G9wGBgv5Qyt60EUChCRWeq1z/++OMAq9X6QWxsUkJUVEyJ1WpzKjNR50LXddxul728vDS2pCS/0O12zxo7duyBtkjbW0UwD/gbsAM4AXhQSvlmWwigUISKzlavf/zxxwEWi+UGTdPO03Ua7tyi6PBoGgW6ri/zeDwvtZUSAO8VwXrgHCllqRAiFlglpRzfVkIoFKFA1WuFwsDb+eQeKWUpgJSyBKgInEgKRdBQ9VqhwPsJZfuEEE8C3wATMeyqnYKWlrsQQtwBXGoefi6lfEgIoQGHgN1m+Hop5R+DKLbXeFG+m4GrMebRPCGlfFcIEQm8CaQCJcB8KWVO/bTbC36WUQNOEkJkAIVAORC83cJ9xJtlWcw4nwEfSSlf6Gz3sVac2mXsMM8ieFVXF2JMuC0xg2YAdmAxEAkcAa6RUpa3pVze9giuAfYBUzGUwPVtKUSIqVnuAvgDxnIXAAghBmGMMJkAnApME0KMxHAubpZSTjI/7bbi0Xz5koGbMMo3BXjSfLBuAn6RUp4JvA7cG3SpfcOfMg4GVgGPAWuA/9K+63WTZazF36COb6DT3Mda1C9jR3oWoeUyjgWm1ypPEXA/sNi8j1uAG9taKG8VQbUjwYqhnTrTcIQ6y11Qd4JGBnCulNItpdQxyl6BcbN6CyFWCyE+F0KIYAvtA02WzxwlM1pK6QR6ABVmOWuuAZZhNADaM/6UcSzQC7gduAjoTfuu183VU4QQczDW8Fje2DV08PsITZaxIz2L0EwZzd7CUOAlIcR3Qohr619DgO6jt4rgJWAQsAIYALzc1oKEkDigqNaxWwhhA5BSOqWUuUIITQjxBLBFSrkLyAQelVJOBh7B6H63V5osH4CU0iWEuAXYwLFy1L6mBIgPhqCtwJ8yZgIFwCfAC8AC2ne9brKMQogTgMswWo5NXdOh72MzZexIzyI0X1ejgX8CVwDnAr81LRABv4/eKoKhUsq7pJQfSinvAIK3q3LgaXa5CyFEBPCWGee3ZvAPwEcAUsq1QC/T3NAeaXE5DynlcxiTqSYKISbXuyYWw4benvGnjD8AYWa9fhRw077rdXNlvAqjR7MKwxdypxDiXDrXfWyqjB3pWYTmy1gOLJRSlpuDF1Zh+BICfh+9HT76PTBJSlluOqDWSClPaSa+zw4R0xbWKB6PR3e7g784XnNkZBzE6XRit4fRt2+/UIuj8INLL72E115bRFRUROHxxx+/C6C5eh0I2mPdVnQe7HZrLq1ddK4WC4GfhBDbgBHAAy3Eb2m/gWqHiFczOd1uncLCNnWSt4r9+/dx99231xz/4x/PMmDAwBBKpPCHWbPmcfHFM+ndu1cMxsiaZh2Nbd3AgfZXtxWdi5SUWK9WbfbKNCSlfAs4BXgYmCClfBtACNHUZjL+OEQ6DM8++0Szx4qGFBTkc//9f6CgoM3WyWo106adx0svLeLWW2/dDYzzol77M+Kjw5GTk8369d+FWgxFEPF6g1IpZb6U8gcpZV6t4NubiO6PQ6TDcOhQRp3jjIz0EEnScXj//bdJS9vBkiVvh1qUOsTFxTN+/PhyL+t1p27gVPPcc0/z1FOPhVoMRRBp7U7VTTll/HGIdBgiIiKaPVbUpaAgn9Wrv0LXdVav/rJd9QqaoKl63akbONXs2LEt1CIogkxrFUFTXq7vgPMBGtlv4DjgOyGEVQhhx2hlbW6lHEGloqKi2WNFXd5//210c09jj8fT7noFjdBUve7UDRyFf+Tl5fLJJ0sJxW6PbUVrFUFTNNhvQAhxpxDiIinlTuANjDHdXwOvSym3B0gORTvg22/X4HIZ70uXy8U336wOsUR+06kbOAr/eO21l3j99VfJyjoaalH8prWb1zfahZZSeoDf1AtOq3X+H8A/Wpm3ooNw5pmTWLlyObquo2kaEydODrVILdGUaWgpcI7ZwNGAa4QQdwJ7pJQfCyGqGzhOVAOny1DtI6xu7HREWtqz+Asp5XQhxANSyocaifJUgORSdCLmzLmUFSuMzZR0XWf27EtbuCKw3HnnLTz11HO8+upLXHvtDY1FaapezwDipJQTaoV51cARQvQDXsV45jTgBiml9Ed+RfukI+8D1FKPIFkI8R5wZv01PKSUl0kpPwmcaIrOQnp63aHMGRnpJCaGbt+UwsJC7r33Hn7+eSvp6Qdxu6sGrl69ejE0Xa/NOQLTga1+ZvtX4Dkp5YdCiOnAo8Asf8ugULQlLSmCKVcLNdYAABzOSURBVMBIjKn3LwZeHEVn5IknHq13/DCvv/5uiKSBhQv/zd69uzl8OIMZM2YRFkbO6tWrXwQQQjyC8cI/hDHJ7NdSygPAOuBDWlj5UQhhxVizaDjGir3jpZRDgbs4NuLIhtr7QNGOaFYRSCkLgW+EEEuklF9XhwshHsVw9CoULVJR4ahz7HA4mogZHGJjYxk9egxnnXU2J500lpSU2FIp5ddCiEUYyxqPxxgqWmO6kVK+I4SY5EXyszHWMDpVCDEQ03RUPYve7Fk/gTE5TdGJ6MCDhlr0ESwArgOGCyHON4Orl6Ju7+t+KxSN8umnH/LJJx9x8OB+NmxYh9vtHLZz586NGGuyfGQOdigUQvhjBhoIfA8gpdwvhDhQfcJc7O554MqO4B9wu91YrdZQi6EIAi2Zht4EvgL+hLG8BBjrgWcDCCHCpZSVgRNPoWh7pk07n7FjT+b1119j/vxriY0N2zd9+vQ5QBLwvLmwooaxrpav/IyxkdNCIUR3oA/UKIGFGPtbeLX+S6jxeDxKEXQRWjINVQIHgEaHVmBsknB2G8ukUASUsLAwevbsxT33/BmAlJTYKillBpAhhFgCZGGYPp2+pi2lXCaEmGIOMT1UK41ngDDgv+a4CymlbPOdptoSj8eN0flXdHYCMo9AoeioSCmfEkJcKP+/vTuPj6q8Fz/+mUkmk50lC2RfWL4iAoobuIBg0YKgfam0tre2pa9btVcv1qutilwVf1Xvz7rbq7Z6r1VREaRaC4pLWWRzaYtAgDyQhDULCYEQQjLJbPePk4QJZJlsc2Yyz/v14mXm5EzON/F75jnnOc/zfJWaIyJfnva9tRhlLbv6Gfe0fC0iFc3bQm6Wscvlxm43O4rgZwnlcaPNetsQhPDjEU0zFBcXy6xZs9YC9yulNuNHXovIg7R/NzxPKbXXn+OKyIsFBTu7FWsgORwO4uLizA4j6LUsLRHKS0z0tiHQtJA3YsQIpZS64vTtSqlJHb1HKfUI8EhXP1spNbyT7/2b0+n+pb9xBprDUQ8kmR1GyAjlO4P+Wn1U00KZzmugru6k2SGElFC+I+htQxC897Wa1nM6r4HDh8vNDiEktHz+u91ucwPphU4bAhEZJSLLRWSxiIzy2f4SgFLq9v4OUNP62sGDB3jggV/zyCP/2aaokM5rqK09VW6hpKTYxEhCidESHD/e5zXlA6arZwR/xFgTxQZ8ICI/VkptAc7q98gCYN261axe/Vmf/KyHHure/Lrp02cwdaoeeWuGJ554lJtvnofL5eL+++/h8ccfjZk4cSIMkLzujcLCUzdDu3bpxVP9YQyzhcrKwyZH0nNdPixWSn0KICJFwJ9F5Lt0MaqiqyLfPvusxJjJ+XLPwte0nrnoIuM5cGZmJvfe+x/5Bw4cyELntbFKbIQNZ2I6JSVFFBcXMWLESLPDCloOh4OjR40qpwUF25gx47smR9QzXTUELhGZA3yklFIicgewgq5nmbQW+W4u4PEUxhK+vn4LmLcEJTB16vQeXZXPnTvnjG2LFj3ezp4Dy0C5g4qIiGDDhi+YPPlSsrNzWbBgwYHbbrttwOR1T23Z8g+2bt2CJyoBi7MBbNG8/vr/8PDDj2K19lcNq9D24Yd/xul0kRLt5qsvN7J//15ycvLMDqvbuvq/+3OMRbQGASil1gC/Apq6eF+HRb4BRORGjKUqVnU/ZE3rnfvvf5B161Zz8mQdANOmTTtBmOe1UoU88+wTeGMG44mKBYsVR8YF7NpVwMsvvxDSRVf6y9at/+S995aQaPMwKMpDgs3D0089zokTtWaH1m2W/hjyJCKvAsuVUh83vz4A5CulXCJyDsb46xuBB4GKrm6hnU63t6amvs/j7A3fu4Jly3RZhs60dwcVTH+zlJSEf3Dah3p7+jqvwfzc9nq9fP75J/zP//4Bd2QsdaOvJnrvegAaZCZRZVuwl33L2LHj+fd/v4ukpGTTYg0W1dVHWL78XT7//BPS49zERbixWuD6vHqe2DqIuPhE5n7/R0yffhU2m7lLdPib235NKBOR3UAWxrpDmRjrpzQCXqVUejtv6azI90+ADIzi3rlAk4jsU0qF3FWUFtpuuul6KisPk5qaMvbQoUMnCKO89nq9FBRs45133mTPHoU7MZ36EVdAZPSpnSwWmjIm4rEnsGPXZubf+Uu+d931zJw5h/j4eNNiN4PT6WTXrh2sW7eaTRu/wOt1c2VGA3PzG3hmm5ESZw1x8eD5NSze4+LVV1/mz8vf5TszZjJ58mVkZGQG9YQzf2cWfwNcpZTa17yi4vNKqR90sv9GYA6w9PQi30qp37R8LSIPY1w5BeXJ0pmzzz4HCI9nA721bNlfg/IOasyYs3nmmd8zfrzsEJFrCIO8bmx0sGnTBlau/JD9+/eCPQ5H7mU4k0d1WGvRlTyKuvhhRB/6hqVL3+aDD5Yzbdp3uPrqWWRlZQf4NwgMj8fDwYP7KSjYxvbtW9m5YxsNjkaiI+GK4Q5mZTeQHOM54325CW4eOO84BUdtfHzQydKlb7N06dukpqQwbvx5nHPOeMaOHW9qhb72+NsQ5DVXaUIpdVhEMrrYv9Mi3z2OVtP6UHl5GWlpxoX/QM5rl8vF9u1b2bRpPZu/3Eijw4E3dgiNuZfiTBoBVp+PAa8XS1M9FncTtspCnCkCFgve6EQaRl6Jtf4ozooCPvlsFZ98spK8/JFMuXwqkyZdSnJyinm/ZC81NjooKSmmqGg3u3btpHBXASfqjGdIw2K9TBrayLnJTZw9xIndZ2VurxeONVppcFlYXWpnWnojFguMS3IyLsnJUYeVLUdsFBxtYvMXVfztb58CkJGezlljzkFkDKNGCenpGaY+kPfrGYGI/C/GiIqvgUuBg0qpX/dzbK0660d97bVX2LevJFChtGo5Zm5ufsCP3XLcefN+0e336b/XKY89tgi328WYMXLw2Wef3USA8xr65xnByZN1lJQUU1JSxI4dBezYWUBTowNLZBSNg3NwJY3EnTC83TsAW+Uuovdvbn3tyJmMM3XMGftZnA1EVpdgP1qE5aQxfDI9I5Px485l9GghP38kaWnpQTfayO12c/hwBaWlByktPcT+/fvYt7eIsvIyPB7jszA11svoxEbGDHExZoiT5Ogzr/xb/O2Qndd3n+om++noOq7MbL9Ei8cL+05EsOuYjcIaG3tqo6hvXqQ8JtpOdk4eubn5ZGXlkJGRSUZGFoMHD+5Vl1KfPiPAqFJ2A0b1pT+13PKKSI7ZRTb27Sthh9qNO3ZoQI9r8RgPgbYdPBLQ4wJE1B/t8Xu//fYfVJQdwh4R2HVR3F4jmfeqbQE9LkCj20JNzTGgbUNw333/ydq1qykr22chyPK6K263m4qKckpLD1JRUd76r6y8jOojVad2jBlM06BcXIMycSdmgLXzQjORNQfPeN1eQ+C1xeAcPhbn8LFYHMexHdvPgdpyyj5bxapVKwCIskeTkZ5BWlo6w4YNZ/jwNNLTM8jIyCIhIeGMn9kXXC4XR49Wc+RIFUeOVFFVVUlVVSWVlRVUVpRzpLoat+fUB3tSDGTHNTEx28WIRBf5iS4GRfl/bmyosBMdHc3s2bNZsWIFGyqcHTYEVgvkJ7rJT3RzTY4DjxfK6yMoqY1kb62DA4cLWFdciMNngFa03U5qaiqpw9JJTR1Gamoqyckprf8SEwf1ybMHvxqC5tJ9y9r51muYXJjGOMEDv9iT1xYT8GP6HL359+4Ze4SXnITQXRelu/afaP/Dz2q1Mn36d0hJSTh81113+fbnm57XHfnoow957bVXzthusUXjjkrAbU/Ak5mNOzYZT+zQ7ueps77NB1uds+u7FW/0IJrSxkPaePB4sDpqsNZX03Symj3Vx9lbsQ0cG84o6hsREcHLL7/G4MF901++fv1ann/+qTO2J9ohxe4iL9rNRVluhsd6SI91MzzWTZytd58dtU1WZs+ezR133IHX6+WLlUv9fq/VAhlxbjLi3FyeZjQeXi8cbbRSXh9B2ckIDjc0cKThBBWF+9n+rZXGdkbxvvDCHxk+PK1Xv4cuTBNmBg8eQk3lwa537GPHm4xU6c7VVl+xWOjuh03Q5nVDQ8OZGyPtOGOG4okehDsuBXfCcLz2no3qsbiamH3tqQ+2pX/9pHs/wGrFEzsUT3Qi3qh4IuoO422IIsISgfW0O1m3243H03G3S3d9/fWX7W6PiYAoq5cIq5cIC0RYvFgtXiIsvc/F2EgvK1aswOv1snLlSoZF9v5nRjTHFmn1EmmBSIsXu9VDTGT7DUFJSZHpDYHp664OHjyEAyfcNJw1y+xQAiam8KMeX0WZ1Ud/vPkZwZD0wB8/l27/3qbndUduuOEHXH/996moKKeoaDdlZaWUl5dRVl5GWdleGit3GTtGJ9I0KAvn0Dw8cSkdjgg6nTcyqs0Hmzcy1v/gXI3Yju4l8th+IusqwOMGi4Xk5FQyRuWQlnYJaWlp5Obmk58/kujo6K5/Zjfcffd91NfXt3YHVVUdpqqqiurqIxypqqTwSCWbDh9rvTGxAMPivOTGNZGT4CI3wUVOgpv4btwlDLJ72F/nYPny5cbrJP8bNq8XKhus7K+LZP+JCPadiGRfXRQnfKY12myRJCclk5yTSnZyKklJyaSkpLb+S0pK7pO5CgOiME1E/VFiCj8K6DEtTuPKzIwuIuMZQc8m9vTkAXNfaFlSQg+37T2LxUJaWnrriKcWLUMed+4sYMuWf7Jt+7dEHd6BN2YwTUkjcSaNxBvVxQe7LRbH8aOtH2wM6iLPvB4ijpdiqy7CVnMAPG5Shw3nwstnM27ceETODuicg9jYWHJycsnJyW33+01NTcazlLJDHDx4gL17S9hTrPiy+FRXa2qsl/yEJkYmuhg92El2vDFhrD3nJTWxrTqqzeuONLiguNbG7ppIimpt7D1h42Tzw+IIq5XMzCwumDCK3Nw8MjIySU/PJCkpOSAP3EO+a8isK9zWUTBZWSYcPdm037undu4sAIxZxsEyj6ATpud1T1itVnJy8sjJyWPmzDk0NNSzefNGPv/bp+zZ/Xfspf/EOTgH57AxuOOHtXuX4BqcReTxQ21et8vlIKpqN/YqBY0niIuLZ8rVM5k+/SpycnKDdvJUVFQU2dk5ZGfnMGnSpa3ba2uPs3dvCSUlRRQV7WH3nl18ucdYVjrOBmcPbmRiShMTk53E+HT/TM9oZNXBGBpcFq7Pr2daetsHxYfrrXxdGcW31XZKaiNxe42GPDsri0nnncWIEaPIzx9JVlY2UVFRmKW3DcHqPomiF/QVrtYPTM/rvhATE8v06TOYPn0G5eVlfPbZx3z++ac0FO7FG5eEY9g5uIbmt2kQnClnYavYgcXdRFPGRGMegQ9L4wmiyrcRVV0MHhdjxoxl5sw5XHDBRaYvp9AbiYmDmDDhPCZMOK91W1VVJYWFO9m+fSvfbvk73+yswWaFc5MamZHpQAa7sFhgiN3DELvRKAA0uY3RROvKY9hbawxUyM/L59rp5zN27DhGjxZiYrrR5RYA/bLWUF8zez2W9uiGwH8DZa2h/hDo3HY4HKxfv5YVK/9CWekhPHHJNORcgifuVBdQSzdrm+duHhdRZVuxHy4gwmrhiqnTmTlzToddMAONx+Nhzx7Fxo1fsGH9Wk7U1TE+yclPR9fxyi6j62vBxFq2HLHxxp5EqhsgNyeXy6dMY/Lky0hJSTUlbn9zu9OGQETeooPbZKXUj3ocXTcFY0MQjEsm9LeeLkPd0i3kq2WJDn/15TLUixYtbFNf1u1uOrpmzZpPILB5DebltsfjYf36tbzx5mvU1h6nMf08mtImgMVyRkNgbThGbPEaLA01XHbZVH7843kkJYVvUfvGxkY+/fRjlr67mAhPIynRLqIjvJyb3MSSojiyMrP42bxbGDduguldZH01oew94FHgl30RlKYFgyuuuJJXXnmRu+++D4CoKKrWrFnzB5PDCiir1crUqdO54IKLefXVl9iwYR2Wpjoacy5tu9/JauJ3f0x8bAx33v1Im66TcGW325kz53tceOHFPLjw19Q6jjExvYF3i+OYNOkS5s+/J+S6ybrsGhKRZ4GNSqn2JpQFRLDdEQR7V0ewCca/13PPPcW4cRNaJpSFTddQe7xeL++88ybvv7+MxrQJRNQZJRcdeVOI3/VXhiTE8uhvnzCteyOYbd68kaef/i8AEuLjeP6FV4NqZdY+W2JCKfWrPokoCA2Uilta9915591mhxA0LBYLP/zhzdTUHGPNms9xxw7Fa40iZt8G7BFeHnrwt7oR6MCkSZcwKDGR47W1fGfGrKBqBLrD33oEHa41q5Q60HfhaFrgVFRUAFBffzTqqquuapPj4ZbXFouFefNu4e9//4Yapxd3Yir2im38aN4vyMjINDu8oGWxWIiOieN4bS2jRo02O5we83f46BKMBee2AmdjlPSrwpiBeUn/hNb/dM3i8PbwwwsoLy8lNzc3B9jAAMnrnoqJieGaa+awZMlivCejiYmN48orrzY7rKBnbZ5tFsrV2/xtCCqBmUqp4yISCyxRSl3bj3FpWr8bMmQITz75PHl5aXtEZApd5LWIWIEXgQkYlcz+VSlV5PP924GfYTQkTyql/F+BLEicf/5FLFmymMgT5Zw7+TLsdrvZIYUAoyGw2cybENZb/jYEGUqp481fNwDDOts5HE4YLfRVVVX59ul2mdfA94BopdTk5gplTwHXAYhIMsbouvOAaGCniCxTSgX/RB0fvhXH8vNHmBhJ6GgZIWr2UNHe8HcRi89EZJ2IPAV8gdFV1JnWEwa4D+OEAdqcMJcAVwJPiUjo/gW1kHXhhRdzxx23sHDhwkz8y+vLgFUASqkv8RmNoZQ6ApyrlHICwwFHqDUCYCwN3eL0tYy0gcuvhkAptQC4B/gKuEUp9QyAiFzcwVsG/Amjhb5bb72d22+/kwkTJpzEv7xOBI77vHaLSOtdtVLKJSJ3AF8Ci/sr7kAZOjR0+7y17vF7WTul1DdKqaVKqV0+mzt6QhpWJ4wWusaMGcvcuXOP+ZnXtYBvaS2rUqrNCvFKqd8DacAUEZnWt9EGVmJiotkhaAHS2/VNO+rSCasTRhtwOsrrjcAsgOZnBNtbviGGPzd3czoxno31XdUVE/R1vQAtePVXYZqNwBxgaXsnDMYV1w0MkBNGG3A6yuv3gRkisgmjsZgnIv8BFCmlPhSRrcDm5vd/rJRaF5hw+0dkZGgtk6D1XH8VpgmrE0YLD821u287bXOhz/cXAYsCGlQ/iowcEHWrND/0S2GacDthtAFHj2Kj7QgirWshPHq082cEIvJa839v7WCXt/s8Im3AiYmJ6fR1oD32mHEN8sEHyzvaRec1uiHorhAo7dKhru4IJonI74C5IpLj+w2l1AKl1Cv9F5o2UDQ0NHT6OtB27NjOf//3c6xZ8zmHD1fg8TRlvPXWW4+BzmtfoTxBKpAyM7MpKysN6Yazq4ZgFnA5MBtQ/R+ONhBlZmZx6NDB1te+s1fN8LvfPcfWrVvYtGk92dk5WCwuBzq/tR6aN+8XiJzF8OFpZofSY101BC8rpa4WkSuUUq8HJCJtwJk//x5+85s727w205NPPs7TT/+eLVv+wcyZs0lJSai++eabdX5rPZKcnMK1115vdhi90lVDkCwiy4DLRaRNv2mgS/ppoSsvL7/1riArK5vc3DxT46mpqWHhwnvZtu1bHn74Adzuprw1a9a8DTqvtfDUVUNwJTAeGAmEVSk/rW/Nn38PDz10v+l3AwDPPfcSxcV7KC09yHXXXR+WpSo1zVdXDUG8UuoLEfkJxsQvTeuRvLx83njjXbPDAKChoZ5zz53IwoWPYLPZiI+3OYFys+PSNLN01RDcDdwFPH/adi8QtnUW09LSKS8va32dnp5hYjRady1Zspj58+/m2Wd/B4Db7czBuOMN67zWwleXxeuDQTAU+PZ1881zcTgcra+jo2N4801dUiFUhXvx+tMtWbKY999fxrvv/sXsULRe6pPi9SKyl7brrjgBG8bS0Wf3KsIQNmXKND77bBVerxeLxcLUqXrNvFAyd+61+E4etlo5p7S0tIQwz+sWP/jBv3DddaE9Ckbrnq5WHz0Lo0bxGuAmpZRgLBa3sb8DC2Y33nhT6zoskZGR3HDDTSZHpHXHW2+9x+LFS5k48XwWLXqU1atXF6DzupXFYiEmJtbsMLQA8qtrSETWKqWu8Hn9hVJqSn8GdpoqYH8Aj9el8vLy7Nra2pTExMSqtLS0A2bHo3XfjTfeKO+9954CcoAUE/IagjC3tQElB0jpaid/F52rEZH/B3yNUWIy0CMsuvxFAi0tLY20tNCdSajB9u3bPxCR7Rh5/TjmjBwKutzWwo+/hWn+BajBWGqiAvgJgIjY+ykuTQsEndeaRi9HDYnIaqWUHm6nDSg6r7Vw01+lKjUtlOm81sJKbxuC4J+EoGndp/NaCyu9bQg0TdO0EKe7hjTtTDqvtbDi1/BREZkD/BxoHU2hlJoF7OynuIKaiFiBF4EJGIvx/atSqsjcqIKfiFwM/H/fOSlm0nndls7rngm2vO4Jf+cRPAncChzz3aiUur3PIwoN3wOilVKTRWQS8BRwnckxBTUR+Q1wM3DS7Fh86LxuS+d1NwVpXnebvw3BDqXU2v4MJMRcBqwCUEp9KSKmLFgWYoqB64E3zQ7Eh87rtnRed18w5nW3+dsQ/EVENgO7WjYopX7ePyGFhETguM9rt4hEKqVcZgUU7JRSy0Uk1+w4TqPzui2d190UpHndbf42BPOBJzBmYWpQCyT4vLbqkyUk6bxuS+d1mPK3IahQSgVHeangsBGYAyxt7kvdbnI8Ws/ovG5L53WY8rchaBCRVcAWmifbKKUW9FtUwe99YIaIbMIYajjP5Hi0ntF53ZbO6zDl7zLUPz19m1Lq9X6JSNMCROe1phlColSlpmma1n/0EhOapmlhTjcEmqZpYU43BJqmaWFONwSapmlhzt/ho1ofEJHvAtlKqT/24L3RQKFSKreb7/sTsKT5ZbeOLSJDge8qpd7uzjG18KNzO7TphiCAlFKrQuzY44FrAX2yaJ3SuR3adEMQQCLyM+As4GxgEBALPKCU+rSD/eOBt4AhQJHP9rXAbUqpQhG5DRgO/AlYBpQDmcDHSqkHTj+2Uuo+EVmIsdJkJPCSUuoPIvI4cAGQBGxVSs0DHgAmiMgtwMfAH4EYoAG4RSl1sA/+LNoAoHM7tOlnBIE3AkjGmMr/QzpvjG8DCpRSU4A/+PGzc4GfARcC00Vk4uk7iMh5wEzgYuAiYLSIDAKOKaVmYJwwk0QkA3gUWN18y/0k8HzzmutPAv/lRzxaeNG5HaL0HUHgFQMrgHcAG/B8J/uOBlYCKKW+EhFnO/v4VtPaqpQ6CiAiXwHSzv4CfK2UcgNu4G4RsQGpIvIOUAfEN8fmaxywQETubT5me7Fo4U3ndojSdwSBNwpIUEpdA/wUeKGTfXcCk6H1aqclgR1AWvPXvldGY0QkVkQiMK6K2qu0VQhMFBGriNhE5DNgFpCllPohsADjFtkCeDiVI4XAvc1XTbdi3Kprmi+d2yFK3xEE3h7gChH5PkYiPtjJvi8Db4jIBoxkbWze/jzwoogcAEp99m/CSOJhwHtKqa0ibS+clFLfNi+0trH5+C8BXwELReQLjMXXSoB0jCu8cSLyK+Ae4KXmER4xwJ09/P21gUvndojSaw0NEM3FMZYopSaZHYum9SWd2/1P3xEEARF5EWO0xelmKqUaAh2PpvUVnduhQd8RaJqmhTn9sFjTNC3M6YZA0zQtzOmGQNM0LczphkDTNC3M6YZA0zQtzP0ftdYIxOyV2SYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_real_feature('m_diff_q1_q2_tf_oof')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "del(m_diff_q1_q2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Нормальная валидация\n",
    "\n",
    "<img src=\"./../images/buben5.jpg\" width=\"70%\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictors = df.columns[7:].tolist()\n",
    "print (predictors)\n",
    "\n",
    "if not os.path.isfile('./../data/tmp/11_model.pkl'):\n",
    "    model = check_model(predictors)\n",
    "    print (model.best_score_)\n",
    "    print (model.best_params_)\n",
    "    with open('./../data/tmp/11_model.pkl', 'wb') as f:\n",
    "        pickle.dump(model, f)\n",
    "else:\n",
    "    with open('./../data/tmp/11_model.pkl', 'rb') as f:\n",
    "        model = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "```python\n",
    "Fitting 5 folds for each of 90 candidates, totalling 450 fits\n",
    "[Parallel(n_jobs=-1)]: Done  18 tasks      | elapsed:   40.9s\n",
    "[Parallel(n_jobs=-1)]: Done 168 tasks      | elapsed:  3.9min\n",
    "[Parallel(n_jobs=-1)]: Done 418 tasks      | elapsed:  9.8min\n",
    "[Parallel(n_jobs=-1)]: Done 450 out of 450 | elapsed: 10.4min finished\n",
    "0.786160924089\n",
    "{'en__l1_ratio': 0.75, 'en__alpha': 1e-05}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred = model.predict_proba(df.loc[ix_test][predictors])[:, 1]\n",
    "y_test_pred_fixed = link_function(y_test_pred)\n",
    "\n",
    "if not os.path.isfile('./../data/tmp/11_pred_fixed.csv'):\n",
    "    pd.DataFrame.from_records(\n",
    "        zip(df.loc[ix_test]['test_id'].values, \n",
    "            y_test_pred_fixed), \n",
    "        columns=['test_id', 'is_duplicate']).to_csv('./../data/tmp/11_pred_fixed.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = plt.subplot2grid((1, 2), (0, 0))\n",
    "ax2 = plt.subplot2grid((1, 2), (0, 1))\n",
    "sns.distplot(y_test_pred, ax=ax1)\n",
    "ax1.set_title('Model prediction')\n",
    "sns.distplot(y_test_pred_fixed, ax=ax2)\n",
    "ax2.set_title('Model prediction fixed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "- результат: **0.33349**\n",
    "- лучший результат был раньше: **0.3316**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "display(\n",
    "    eli5.sklearn.explain_linear_classifier_weights(\n",
    "        model.best_estimator_.steps[1][1],\n",
    "        feature_names=predictors, top=30))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "$$\\large\\begin{array}{rcl}\n",
    "t_0 &=& \\frac{n_0}{n_0 + n_1} \\Rightarrow \\\\\n",
    "n_1 &=& \\frac{n_0 t_1}{t_0}\n",
    "\\end{array}$$\n",
    "\n",
    "- у трейн и тест сплита своя пропорция $t_i$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class QuoraKFold():\n",
    "    \n",
    "    def __init__(self, n_splits=10):\n",
    "        self.n_splits = n_splits\n",
    "        \n",
    "    def get_n_splits(self, X=None, y=None, groups=None):\n",
    "        return self.n_splits\n",
    "        \n",
    "    def split(self, X, y, groups=None):\n",
    "        r_split = 1 - 1.0/self.n_splits\n",
    "        k = r1*d[0]/(r0*d[1])\n",
    "        ix_ones = np.where(y == 1)[0]\n",
    "        ix_zeros = np.where(y == 0)[0]\n",
    "        for i in range(self.n_splits):\n",
    "            ix_first_zeros = np.random.choice(\n",
    "                ix_zeros, \n",
    "                size=int(r_split * ix_zeros.shape[0]), \n",
    "                replace=False)\n",
    "            ix_first_ones = np.random.choice(\n",
    "                ix_ones,\n",
    "                size=int(ix_first_zeros.shape[0] * d[1]/d[0]),\n",
    "                replace=False)\n",
    "            \n",
    "            ix_second_zeros = np.setdiff1d(ix_zeros, ix_first_zeros)\n",
    "            ix_second_ones = np.random.choice(\n",
    "                np.setdiff1d(ix_ones, ix_first_ones),\n",
    "                size=int(ix_second_zeros.shape[0] * r1/r0),\n",
    "                replace=False)\n",
    "            \n",
    "            ix_first = np.hstack((ix_first_zeros, ix_first_ones))\n",
    "            ix_first = ix_first[np.random.choice(\n",
    "                range(ix_first.shape[0]), size=ix_first.shape[0], replace=False)]\n",
    "            \n",
    "            ix_second = np.hstack((ix_second_zeros, ix_second_ones))\n",
    "            ix_second = ix_second[np.random.choice(\n",
    "                range(ix_second.shape[0]), size=ix_second.shape[0], replace=False)]\n",
    "            \n",
    "            yield ix_first, ix_second"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def log_loss_lf(y_true, y_pred):\n",
    "    return log_loss(y_true, link_function(y_pred[:, 1]), eps=eps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_model(predictors, data=None, do_scaling=True, folder=None):\n",
    "    classifier = lambda: SGDClassifier(\n",
    "        loss='log', \n",
    "        penalty='elasticnet', \n",
    "        fit_intercept=True, \n",
    "        n_iter=100, \n",
    "        shuffle=True, \n",
    "        n_jobs=-1,\n",
    "        class_weight=None)\n",
    "\n",
    "    steps = []\n",
    "    if do_scaling:\n",
    "        steps.append(('ss', StandardScaler()))\n",
    "    steps.append(('en', classifier()))\n",
    "    \n",
    "    model = Pipeline(steps=steps)\n",
    "\n",
    "    parameters = {\n",
    "        'en__alpha': [0.00001, 0.0001, 0.001, 0.01, 0.02, 0.1, 0.5, 0.9, 1],\n",
    "        'en__l1_ratio': [0, 0.0001, 0.001, 0.01, 0.1, 0.3, 0.5, 0.75, 0.9, 1]\n",
    "    }\n",
    "\n",
    "    if folder is None:\n",
    "        folder = QuoraKFold(n_splits=10)\n",
    "    \n",
    "    grid_search = GridSearchCV(\n",
    "        model, \n",
    "        parameters, \n",
    "        cv=folder, \n",
    "        n_jobs=-1,\n",
    "        scoring=make_scorer(log_loss_lf, greater_is_better=False, needs_proba=True),\n",
    "        verbose=1)\n",
    "    if data is None:\n",
    "        grid_search = grid_search.fit(df.loc[ix_train][predictors], \n",
    "                                      df.loc[ix_train]['is_duplicate'])\n",
    "    else:\n",
    "        grid_search = grid_search.fit(data['X'], \n",
    "                                      data['y'])\n",
    "    \n",
    "    return grid_search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.isfile('./../data/tmp/12_model.pkl'):\n",
    "    model = check_model(predictors, folder=StratifiedKFold(n_splits=5, shuffle=True))\n",
    "    print (-model.best_score_)\n",
    "    print (model.best_params_)\n",
    "    with open('./../data/tmp/12_model.pkl', 'wb') as f:\n",
    "        pickle.dump(model, f)\n",
    "else:\n",
    "    with open('./../data/tmp/12_model.pkl', 'rb') as f:\n",
    "        model = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "Fitting 5 folds for each of 90 candidates, totalling 450 fits\n",
    "[Parallel(n_jobs=-1)]: Done  18 tasks      | elapsed:   40.5s\n",
    "[Parallel(n_jobs=-1)]: Done 168 tasks      | elapsed:  4.0min\n",
    "[Parallel(n_jobs=-1)]: Done 418 tasks      | elapsed:  9.8min\n",
    "[Parallel(n_jobs=-1)]: Done 450 out of 450 | elapsed: 10.5min finished\n",
    "0.50136321542\n",
    "{'en__l1_ratio': 0.01, 'en__alpha': 1e-05}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.isfile('./../data/tmp/13_model.pkl'):\n",
    "    model = check_model(predictors)\n",
    "    print (-model.best_score_)\n",
    "    print (model.best_params_)\n",
    "    with open('./../data/tmp/13_model.pkl', 'wb') as f:\n",
    "        pickle.dump(model, f)\n",
    "else:\n",
    "    with open('./../data/tmp/13_model.pkl', 'rb') as f:\n",
    "        model = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "```python\n",
    "Fitting 10 folds for each of 90 candidates, totalling 900 fits\n",
    "[Parallel(n_jobs=-1)]: Done  18 tasks      | elapsed:   46.7s\n",
    "[Parallel(n_jobs=-1)]: Done 168 tasks      | elapsed:  4.5min\n",
    "[Parallel(n_jobs=-1)]: Done 418 tasks      | elapsed: 10.9min\n",
    "[Parallel(n_jobs=-1)]: Done 768 tasks      | elapsed: 19.9min\n",
    "[Parallel(n_jobs=-1)]: Done 900 out of 900 | elapsed: 23.4min finished\n",
    "0.323176002744\n",
    "{'en__l1_ratio': 1, 'en__alpha': 0.0001}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred = model.predict_proba(df.loc[ix_test][predictors])[:, 1]\n",
    "y_test_pred_fixed = link_function(y_test_pred)\n",
    "\n",
    "if not os.path.isfile('./../data/tmp/13_pred_fixed.csv'):\n",
    "    pd.DataFrame.from_records(\n",
    "        zip(df.loc[ix_test]['test_id'].values, \n",
    "            y_test_pred_fixed), \n",
    "        columns=['test_id', 'is_duplicate']).to_csv('./../data/tmp/13_pred_fixed.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = plt.subplot2grid((1, 2), (0, 0))\n",
    "ax2 = plt.subplot2grid((1, 2), (0, 1))\n",
    "sns.distplot(y_test_pred, ax=ax1)\n",
    "ax1.set_title('Model prediction')\n",
    "sns.distplot(y_test_pred_fixed, ax=ax2)\n",
    "ax2.set_title('Model prediction fixed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "- результат: **0.33524** почти как в валидации\n",
    "- лучший результат был раньше: **0.3316**\n",
    "- будем надеяться что это тоже погрешность"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Факторизация вертикально сконкатенированной матрицы\n",
    "\n",
    "<img src=\"./../images/mf3.png\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.isfile('./../data/tmp/2_svd.pkl'):\n",
    "    svd = TruncatedSVD(n_components=100)\n",
    "    m_svd = svd.fit_transform(sparse.csc_matrix(sparse.vstack((m_q1_tf, m_q2_tf))))\n",
    "    with open('./../data/tmp/2_svd.pkl', 'wb') as f:\n",
    "        pickle.dump(svd, f)\n",
    "    with open('./../data/tmp/2_m_svd.npz', 'wb') as f:\n",
    "        np.savez(f, m_svd)\n",
    "else:\n",
    "    with open('./../data/tmp/2_svd.pkl', 'rb') as f:\n",
    "        svd = pickle.load(f)\n",
    "    with open('./../data/tmp/2_m_svd.npz', 'rb') as f:\n",
    "        m_svd = np.load(f)['arr_0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "del(m_q1_tf, m_q2_tf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "m_svd_q1 = m_svd[:m_svd.shape[0]//2, :]\n",
    "m_svd_q2 = m_svd[m_svd.shape[0]//2:, :]\n",
    "del(m_svd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['m_vstack_svd_q1_q2_euclidean'] = ((m_svd_q1 - m_svd_q2)**2).mean(axis=1)\n",
    "plot_real_feature('m_vstack_svd_q1_q2_euclidean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "num = (m_svd_q1*m_svd_q2).sum(axis=1)\n",
    "den = np.sqrt((m_svd_q1**2).sum(axis=1))*np.sqrt((m_svd_q2**2).sum(axis=1))\n",
    "num[np.where(den == 0)] = 0\n",
    "den[np.where(den == 0)] = 1\n",
    "df['m_vstack_svd_q1_q2_cosine'] = 1 - num/den\n",
    "plot_real_feature('m_vstack_svd_q1_q2_cosine')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### OOF от произведения svd вертикальной конкатенации"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "m_svd = m_svd_q1*m_svd_q2\n",
    "\n",
    "if not os.path.isfile('./../data/tmp/14_model.pkl'):\n",
    "    model = check_model(None, data={\n",
    "        'X': m_svd[ix_train, :],\n",
    "        'y': df.loc[ix_train]['is_duplicate']\n",
    "    }, do_scaling=False)\n",
    "    print (model.best_score_)\n",
    "    print (model.best_params_)\n",
    "    with open('./../data/tmp/14_model.pkl', 'wb') as f:\n",
    "        pickle.dump(model, f)\n",
    "else:\n",
    "    with open('./../data/tmp/14_model.pkl', 'rb') as f:\n",
    "        model = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "Fitting 10 folds for each of 90 candidates, totalling 900 fits\n",
    "[Parallel(n_jobs=-1)]: Done  18 tasks      | elapsed:  2.1min\n",
    "[Parallel(n_jobs=-1)]: Done 168 tasks      | elapsed: 12.3min\n",
    "[Parallel(n_jobs=-1)]: Done 418 tasks      | elapsed: 31.6min\n",
    "[Parallel(n_jobs=-1)]: Done 768 tasks      | elapsed: 60.1min\n",
    "[Parallel(n_jobs=-1)]: Done 900 out of 900 | elapsed: 70.9min finished\n",
    "-0.409563471096\n",
    "{'en__l1_ratio': 1, 'en__alpha': 1e-05}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data={\n",
    "    'X_train': m_svd[ix_train, :],\n",
    "    'y_train': df.loc[ix_train]['is_duplicate'],\n",
    "    'X_test': m_svd[ix_test, :],\n",
    "    'y_train_pred': np.zeros(ix_train.shape[0]),\n",
    "    'y_test_pred': []\n",
    "}\n",
    "del(m_svd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.isfile('./../data/tmp/15_model_pred.pkl'):\n",
    "    n_splits = 10\n",
    "    folder = StratifiedKFold(n_splits=n_splits, shuffle=True)\n",
    "    for ix_first, ix_second in tqdm_notebook(folder.split(np.zeros(data['y_train'].shape[0]), data['y_train']), \n",
    "                                             total=n_splits):\n",
    "        # {'en__l1_ratio': 1, 'en__alpha': 1e-05}\n",
    "        model = SGDClassifier(\n",
    "            loss='log', \n",
    "            penalty='elasticnet', \n",
    "            fit_intercept=True, \n",
    "            n_iter=100, \n",
    "            shuffle=True, \n",
    "            n_jobs=-1,\n",
    "            l1_ratio=1.0,\n",
    "            alpha=1e-05,\n",
    "            class_weight=None)\n",
    "        model = model.fit(data['X_train'][ix_first, :], data['y_train'][ix_first])\n",
    "        data['y_train_pred'][ix_second] = model.predict_proba(data['X_train'][ix_second, :])[:, 1]\n",
    "        data['y_test_pred'].append(model.predict_proba(data['X_test'])[:, 1])\n",
    "        \n",
    "    data['y_test_pred'] = np.array(data['y_test_pred']).T.mean(axis=1)\n",
    "    with open('./../data/tmp/15_model_pred.pkl', 'wb') as f:\n",
    "        pickle.dump({\n",
    "            'model': model,\n",
    "            'y_train_pred': data['y_train_pred'],\n",
    "            'y_test_pred': data['y_test_pred']\n",
    "        }, f)\n",
    "else:\n",
    "    with open('./../data/tmp/15_model_pred.pkl', 'rb') as f:\n",
    "        tmp = pickle.load(f)\n",
    "        model = tmp['model']\n",
    "        data['y_train_pred'] = tmp['y_train_pred']\n",
    "        data['y_test_pred'] = tmp['y_test_pred']\n",
    "        del(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = plt.subplot2grid((1, 2), (0, 0))\n",
    "ax2 = plt.subplot2grid((1, 2), (0, 1))\n",
    "\n",
    "sns.distplot(data['y_train_pred'], label='train', ax=ax1)\n",
    "sns.distplot(data['y_test_pred'], label='test', ax=ax1)\n",
    "ax1.axvline(np.mean(data['y_train_pred']), color='b', alpha=1, linestyle='--', label='mean(train)')\n",
    "ax1.axvline(np.mean(data['y_test_pred']), color='g', alpha=1, linestyle='--', label='mean(test)')\n",
    "ax1.legend(loc='upper right', prop={'size': 18})\n",
    "ax1.set_title('Train/test OOF predictions')\n",
    "\n",
    "sns.distplot(data['y_train_pred'][:ix_train.shape[0]//2], label='train first part', ax=ax2)\n",
    "sns.distplot(data['y_train_pred'][ix_train.shape[0]//2:], label='train second part', ax=ax2)\n",
    "ax2.legend(loc='upper right', prop={'size': 18})\n",
    "ax2.set_title('Train/train OOF predictions')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mp = np.mean(data['y_train_pred'])\n",
    "print (mp)\n",
    "\n",
    "def func(w):\n",
    "    return (mp*data['y_test_pred'].shape[0] - \n",
    "            np.sum(w[0]*data['y_test_pred']/(w[0]*data['y_test_pred'] + \n",
    "                                             (1 - w[0]) * (1 - data['y_test_pred']))))**2\n",
    "\n",
    "print (func(np.array([1])))\n",
    "\n",
    "res = minimize(func, np.array([1]), method='L-BFGS-B', bounds=[(0, 1)])\n",
    "\n",
    "print(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "w = res['x'][0]\n",
    "\n",
    "def fix_function(x):\n",
    "    return w*x/(w*x + (1 - w)*(1 - x))\n",
    "\n",
    "support = np.linspace(0, 1, 1000)\n",
    "values = fix_function(support)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(support, values)\n",
    "ax.set_title('Fix transformation', fontsize=20)\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('f(x)')\n",
    "plt.show()\n",
    "\n",
    "data['y_test_pred_fixed'] = fix_function(data['y_test_pred'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = plt.subplot2grid((1, 2), (0, 0))\n",
    "ax2 = plt.subplot2grid((1, 2), (0, 1))\n",
    "\n",
    "sns.distplot(data['y_train_pred'], label='train', ax=ax1)\n",
    "sns.distplot(data['y_test_pred_fixed'], label='test', ax=ax1)\n",
    "ax1.axvline(np.mean(data['y_train_pred']), color='b', alpha=1, linestyle='--', label='mean(train)')\n",
    "ax1.axvline(np.mean(data['y_test_pred_fixed']), color='g', alpha=1, linestyle='--', label='mean(test)')\n",
    "ax1.legend(loc='upper right', prop={'size': 18})\n",
    "ax1.set_title('Train/test OOF predictions')\n",
    "\n",
    "sns.distplot(data['y_train_pred'][:ix_train.shape[0]//2], label='train first part', ax=ax2)\n",
    "sns.distplot(data['y_train_pred'][ix_train.shape[0]//2:], label='train second part', ax=ax2)\n",
    "ax2.legend(loc='upper right', prop={'size': 18})\n",
    "ax2.set_title('Train/train OOF predictions')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['m_vstack_svd_mult_q1_q2_oof'] = np.zeros(df.shape[0])\n",
    "df.loc[ix_train, 'm_vstack_svd_mult_q1_q2_oof'] = data['y_train_pred']\n",
    "df.loc[ix_test, 'm_vstack_svd_mult_q1_q2_oof'] = data['y_test_pred_fixed']\n",
    "del(data)\n",
    "plot_real_feature('m_vstack_svd_mult_q1_q2_oof')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### OOF от разницы svd вертикальной конкатенации"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "m_svd = np.abs(m_svd_q1 - m_svd_q2)\n",
    "\n",
    "if not os.path.isfile('./../data/tmp/16_model.pkl'):\n",
    "    model = check_model(None, data={\n",
    "        'X': m_svd[ix_train, :],\n",
    "        'y': df.loc[ix_train]['is_duplicate']\n",
    "    }, do_scaling=False)\n",
    "    print (model.best_score_)\n",
    "    print (model.best_params_)\n",
    "    with open('./../data/tmp/16_model.pkl', 'wb') as f:\n",
    "        pickle.dump(model, f)\n",
    "else:\n",
    "    with open('./../data/tmp/16_model.pkl', 'rb') as f:\n",
    "        model = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "```python\n",
    "\n",
    "[Parallel(n_jobs=-1)]: Done  18 tasks      | elapsed:  2.1min\n",
    "[Parallel(n_jobs=-1)]: Done 168 tasks      | elapsed: 12.5min\n",
    "[Parallel(n_jobs=-1)]: Done 418 tasks      | elapsed: 30.0min\n",
    "[Parallel(n_jobs=-1)]: Done 768 tasks      | elapsed: 54.6min\n",
    "[Parallel(n_jobs=-1)]: Done 900 out of 900 | elapsed: 63.4min finished\n",
    "-0.439305223632\n",
    "{'en__l1_ratio': 0.01, 'en__alpha': 1e-05}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data={\n",
    "    'X_train': m_svd[ix_train, :],\n",
    "    'y_train': df.loc[ix_train]['is_duplicate'],\n",
    "    'X_test': m_svd[ix_test, :],\n",
    "    'y_train_pred': np.zeros(ix_train.shape[0]),\n",
    "    'y_test_pred': []\n",
    "}\n",
    "del(m_svd, m_svd_q1, m_svd_q2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.isfile('./../data/tmp/17_model_pred.pkl'):\n",
    "    n_splits = 10\n",
    "    folder = StratifiedKFold(n_splits=n_splits, shuffle=True)\n",
    "    for ix_first, ix_second in tqdm_notebook(folder.split(np.zeros(data['y_train'].shape[0]), data['y_train']), \n",
    "                                             total=n_splits):\n",
    "        # {'en__l1_ratio': 0.01, 'en__alpha': 1e-05}\n",
    "        model = SGDClassifier(\n",
    "            loss='log', \n",
    "            penalty='elasticnet', \n",
    "            fit_intercept=True, \n",
    "            n_iter=100, \n",
    "            shuffle=True, \n",
    "            n_jobs=-1,\n",
    "            l1_ratio=0.01,\n",
    "            alpha=1e-05,\n",
    "            class_weight=None)\n",
    "        model = model.fit(data['X_train'][ix_first, :], data['y_train'][ix_first])\n",
    "        data['y_train_pred'][ix_second] = model.predict_proba(data['X_train'][ix_second, :])[:, 1]\n",
    "        data['y_test_pred'].append(model.predict_proba(data['X_test'])[:, 1])\n",
    "        \n",
    "    data['y_test_pred'] = np.array(data['y_test_pred']).T.mean(axis=1)\n",
    "    with open('./../data/tmp/17_model_pred.pkl', 'wb') as f:\n",
    "        pickle.dump({\n",
    "            'model': model,\n",
    "            'y_train_pred': data['y_train_pred'],\n",
    "            'y_test_pred': data['y_test_pred']\n",
    "        }, f)\n",
    "else:\n",
    "    with open('./../data/tmp/17_model_pred.pkl', 'rb') as f:\n",
    "        tmp = pickle.load(f)\n",
    "        model = tmp['model']\n",
    "        data['y_train_pred'] = tmp['y_train_pred']\n",
    "        data['y_test_pred'] = tmp['y_test_pred']\n",
    "        del(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = plt.subplot2grid((1, 2), (0, 0))\n",
    "ax2 = plt.subplot2grid((1, 2), (0, 1))\n",
    "\n",
    "sns.distplot(data['y_train_pred'], label='train', ax=ax1)\n",
    "sns.distplot(data['y_test_pred'], label='test', ax=ax1)\n",
    "ax1.axvline(np.mean(data['y_train_pred']), color='b', alpha=1, linestyle='--', label='mean(train)')\n",
    "ax1.axvline(np.mean(data['y_test_pred']), color='g', alpha=1, linestyle='--', label='mean(test)')\n",
    "ax1.legend(loc='upper right', prop={'size': 18})\n",
    "ax1.set_title('Train/test OOF predictions')\n",
    "\n",
    "sns.distplot(data['y_train_pred'][:ix_train.shape[0]//2], label='train first part', ax=ax2)\n",
    "sns.distplot(data['y_train_pred'][ix_train.shape[0]//2:], label='train second part', ax=ax2)\n",
    "ax2.legend(loc='upper right', prop={'size': 18})\n",
    "ax2.set_title('Train/train OOF predictions')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mp = np.mean(data['y_train_pred'])\n",
    "print (mp)\n",
    "\n",
    "def func(w):\n",
    "    return (mp*data['y_test_pred'].shape[0] - \n",
    "            np.sum(w[0]*data['y_test_pred']/(w[0]*data['y_test_pred'] + \n",
    "                                             (1 - w[0]) * (1 - data['y_test_pred']))))**2\n",
    "\n",
    "print (func(np.array([1])))\n",
    "\n",
    "res = minimize(func, np.array([1]), method='L-BFGS-B', bounds=[(0, 1)])\n",
    "\n",
    "print (res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "w = res['x'][0]\n",
    "\n",
    "def fix_function(x):\n",
    "    return w*x/(w*x + (1 - w)*(1 - x))\n",
    "\n",
    "support = np.linspace(0, 1, 1000)\n",
    "values = fix_function(support)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(support, values)\n",
    "ax.set_title('Fix transformation', fontsize=20)\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('f(x)')\n",
    "plt.show()\n",
    "\n",
    "data['y_test_pred_fixed'] = fix_function(data['y_test_pred'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = plt.subplot2grid((1, 2), (0, 0))\n",
    "ax2 = plt.subplot2grid((1, 2), (0, 1))\n",
    "\n",
    "sns.distplot(data['y_train_pred'], label='train', ax=ax1)\n",
    "sns.distplot(data['y_test_pred_fixed'], label='test', ax=ax1)\n",
    "ax1.axvline(np.mean(data['y_train_pred']), color='b', alpha=1, linestyle='--', label='mean(train)')\n",
    "ax1.axvline(np.mean(data['y_test_pred_fixed']), color='g', alpha=1, linestyle='--', label='mean(test)')\n",
    "ax1.legend(loc='upper right', prop={'size': 18})\n",
    "ax1.set_title('Train/test OOF predictions')\n",
    "\n",
    "sns.distplot(data['y_train_pred'][:ix_train.shape[0]//2], label='train first part', ax=ax2)\n",
    "sns.distplot(data['y_train_pred'][ix_train.shape[0]//2:], label='train second part', ax=ax2)\n",
    "ax2.legend(loc='upper right', prop={'size': 18})\n",
    "ax2.set_title('Train/train OOF predictions')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['m_vstack_svd_absdiff_q1_q2_oof'] = np.zeros(df.shape[0])\n",
    "df.loc[ix_train, 'm_vstack_svd_absdiff_q1_q2_oof'] = data['y_train_pred']\n",
    "df.loc[ix_test, 'm_vstack_svd_absdiff_q1_q2_oof'] = data['y_test_pred']\n",
    "del(data)\n",
    "plot_real_feature('m_vstack_svd_absdiff_q1_q2_oof')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Обрабатываем слова целиком\n",
    "\n",
    "<img src=\"./../images/buben6.jpg\" width=\"70%\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Лемматизированные слова"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp = spacy.load('en')\n",
    "df.head()['question1'].apply(lambda s: ' '.join([c.lemma_ for c in nlp(s) if c.lemma_  != '?']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "SYMBOLS = set(' '.join(string.punctuation).split(' ') + ['...', '“', '”', '\\'ve'])\n",
    "\n",
    "if not os.path.isfile('./../data/tmp/bow_lemma.pkl'):\n",
    "    q1 = []\n",
    "\n",
    "    for doc in nlp.pipe(df['question1'].values.tolist(), n_threads=16, batch_size=10000):\n",
    "        q1.append([c.lemma_ for c in doc if c.lemma_ not in SYMBOLS])\n",
    "\n",
    "    q2 = []\n",
    "\n",
    "    for doc in nlp.pipe(df['question2'].values.tolist(), n_threads=16, batch_size=10000):\n",
    "        q2.append([c.lemma_ for c in doc if c.lemma_ not in SYMBOLS])\n",
    "        \n",
    "    with open('./../data/tmp/bow_lemma.pkl', 'wb') as f:\n",
    "        pickle.dump({\n",
    "            'q1': q1,\n",
    "            'q2': q2\n",
    "        }, f)\n",
    "else:\n",
    "    with open('./../data/tmp/bow_lemma.pkl', 'rb') as f:\n",
    "        tmp = pickle.load(f)\n",
    "        q1 = tmp['q1']\n",
    "        q2 = tmp['q2']\n",
    "        del(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "if os.path.isfile('./../data/tmp/cv_word_lemma.pkl') and os.path.isfile('./../data/tmp/wl_freq.pkl'):\n",
    "    with open('./../data/tmp/cv_word_lemma.pkl', 'rb') as f:\n",
    "        cv_words = pickle.load(f)\n",
    "    with open('./../data/tmp/wl_freq.pkl', 'rb') as f:\n",
    "        w_freq = pickle.load(f)\n",
    "else:\n",
    "    cv_words = CountVectorizer(ngram_range=(1, 1), analyzer='word')\n",
    "    w_freq = np.array(cv_words.fit_transform(\n",
    "        [' '.join(s) for s in q1] + [' '.join(s) for s in q2]).sum(axis=0))[0, :]\n",
    "    with open('./../data/tmp/cv_word_lemma.pkl', 'wb') as f:\n",
    "        pickle.dump(cv_words, f)\n",
    "    with open('./../data/tmp/wl_freq.pkl', 'wb') as f:\n",
    "        pickle.dump(w_freq, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if os.path.isfile('./../data/tmp/m_q1_wl.npz') and os.path.isfile('./../data/tmp/m_q2_wl.npz'):\n",
    "    m_q1 = load_sparse_csr('./../data/tmp/m_q1_wl.npz')\n",
    "    m_q2 = load_sparse_csr('./../data/tmp/m_q2_wl.npz')\n",
    "else:\n",
    "    m_q1 = cv_words.transform([' '.join(s) for s in q1])\n",
    "    m_q2 = cv_words.transform([' '.join(s) for s in q2])\n",
    "    save_sparse_csr('./../data/tmp/m_q1_wl.npz', m_q1)\n",
    "    save_sparse_csr('./../data/tmp/m_q2_wl.npz', m_q2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tft = TfidfTransformer(\n",
    "    norm='l2', \n",
    "    use_idf=True, \n",
    "    smooth_idf=True, \n",
    "    sublinear_tf=False)\n",
    "\n",
    "tft = tft.fit(sparse.vstack((m_q1, m_q2)))\n",
    "m_q1_tf = tft.transform(m_q1)\n",
    "m_q2_tf = tft.transform(m_q2)\n",
    "\n",
    "v_num = np.array(m_q1_tf.multiply(m_q2_tf).sum(axis=1))[:, 0]\n",
    "v_den = np.array(np.sqrt(m_q1_tf.multiply(m_q1_tf).sum(axis=1)))[:, 0] * \\\n",
    "        np.array(np.sqrt(m_q2_tf.multiply(m_q2_tf).sum(axis=1)))[:, 0]\n",
    "v_num[np.where(v_den == 0)] = 1\n",
    "v_den[np.where(v_den == 0)] = 1\n",
    "\n",
    "v_score = 1 - v_num/v_den\n",
    "\n",
    "df['1wl_tfidf_cosine'] = v_score\n",
    "plot_real_feature('1wl_tfidf_cosine')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tft = TfidfTransformer(\n",
    "    norm='l2', \n",
    "    use_idf=True, \n",
    "    smooth_idf=True, \n",
    "    sublinear_tf=False)\n",
    "\n",
    "tft = tft.fit(sparse.vstack((m_q1, m_q2)))\n",
    "m_q1_tf = tft.transform(m_q1)\n",
    "m_q2_tf = tft.transform(m_q2)\n",
    "\n",
    "v_score = (m_q1_tf - m_q2_tf)\n",
    "v_score = np.sqrt(np.array(v_score.multiply(v_score).sum(axis=1))[:, 0])\n",
    "\n",
    "df['1wl_tfidf_l2_euclidean'] = v_score\n",
    "plot_real_feature('1wl_tfidf_l2_euclidean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tft = TfidfTransformer(\n",
    "    norm='l2', \n",
    "    use_idf=False, \n",
    "    smooth_idf=True, \n",
    "    sublinear_tf=False)\n",
    "\n",
    "tft = tft.fit(sparse.vstack((m_q1, m_q2)))\n",
    "m_q1_tf = tft.transform(m_q1)\n",
    "m_q2_tf = tft.transform(m_q2)\n",
    "\n",
    "v_score = (m_q1_tf - m_q2_tf)\n",
    "v_score = np.sqrt(np.array(v_score.multiply(v_score).sum(axis=1))[:, 0])\n",
    "\n",
    "df['1wl_tf_l2_euclidean'] = v_score\n",
    "plot_real_feature('1wl_tf_l2_euclidean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tft = TfidfTransformer(\n",
    "    norm='l2', \n",
    "    use_idf=True, \n",
    "    smooth_idf=True, \n",
    "    sublinear_tf=False)\n",
    "\n",
    "tft = tft.fit(sparse.vstack((m_q1, m_q2)))\n",
    "m_q1_tf = tft.transform(m_q1)\n",
    "m_q2_tf = tft.transform(m_q2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.isfile('./../data/tmp/20_model.pkl'):\n",
    "    model = check_model(None, data={\n",
    "        'X': sparse.csc_matrix(sparse.hstack((m_q1_tf, m_q2_tf)))[ix_train, :],\n",
    "        'y': df.loc[ix_train]['is_duplicate']\n",
    "    }, do_scaling=False)\n",
    "    print (model.best_score_)\n",
    "    print (model.best_params_)\n",
    "    with open('./../data/tmp/20_model.pkl', 'wb') as f:\n",
    "        pickle.dump(model, f)\n",
    "else:\n",
    "    with open('./../data/tmp/20_model.pkl', 'rb') as f:\n",
    "        model = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "```python\n",
    "Fitting 10 folds for each of 90 candidates, totalling 900 fits\n",
    "[Parallel(n_jobs=-1)]: Done  18 tasks      | elapsed:  1.4min\n",
    "[Parallel(n_jobs=-1)]: Done 168 tasks      | elapsed:  8.2min\n",
    "[Parallel(n_jobs=-1)]: Done 418 tasks      | elapsed: 19.2min\n",
    "[Parallel(n_jobs=-1)]: Done 768 tasks      | elapsed: 34.4min\n",
    "[Parallel(n_jobs=-1)]: Done 900 out of 900 | elapsed: 39.9min finished\n",
    "-0.365063639733\n",
    "{'en__l1_ratio': 0.0001, 'en__alpha': 1e-05}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred = model.predict_proba(sparse.csc_matrix(sparse.hstack((m_q1_tf, m_q2_tf)))[ix_test, :])[:, 1]\n",
    "y_test_pred_fixed = link_function(y_test_pred)\n",
    "\n",
    "if not os.path.isfile('./../data/tmp/20_pred_fixed.csv'):\n",
    "    pd.DataFrame.from_records(\n",
    "        zip(df.loc[ix_test]['test_id'].values, \n",
    "            y_test_pred_fixed), \n",
    "        columns=['test_id', 'is_duplicate']).to_csv('./../data/tmp/20_pred_fixed.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- старый результат: **0.3316**\n",
    "- внезапно новый результат: **0.30964**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data={\n",
    "    'X_train': sparse.csc_matrix(sparse.hstack((m_q1_tf, m_q2_tf)))[ix_train, :],\n",
    "    'y_train': df.loc[ix_train]['is_duplicate'],\n",
    "    'X_test': sparse.csc_matrix(sparse.hstack((m_q1_tf, m_q2_tf)))[ix_test, :],\n",
    "    'y_train_pred': np.zeros(ix_train.shape[0]),\n",
    "    'y_test_pred': []\n",
    "}\n",
    "del(m_q1_tf, m_q2_tf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.isfile('./../data/tmp/21_model_pred.pkl'):\n",
    "    n_splits = 10\n",
    "    folder = StratifiedKFold(n_splits=n_splits, shuffle=True)\n",
    "    for ix_first, ix_second in tqdm_notebook(folder.split(np.zeros(data['y_train'].shape[0]), data['y_train']), \n",
    "                                             total=n_splits):\n",
    "        # {'en__l1_ratio': 0.0001, 'en__alpha': 1e-05}\n",
    "        model = SGDClassifier(\n",
    "            loss='log', \n",
    "            penalty='elasticnet', \n",
    "            fit_intercept=True, \n",
    "            n_iter=100, \n",
    "            shuffle=True, \n",
    "            n_jobs=-1,\n",
    "            l1_ratio=0.0001,\n",
    "            alpha=1e-05,\n",
    "            class_weight=None)\n",
    "        model = model.fit(data['X_train'][ix_first, :], data['y_train'][ix_first])\n",
    "        data['y_train_pred'][ix_second] = model.predict_proba(data['X_train'][ix_second, :])[:, 1]\n",
    "        data['y_test_pred'].append(model.predict_proba(data['X_test'])[:, 1])\n",
    "        \n",
    "    data['y_test_pred'] = np.array(data['y_test_pred']).T.mean(axis=1)\n",
    "    with open('./../data/tmp/21_model_pred.pkl', 'wb') as f:\n",
    "        pickle.dump({\n",
    "            'model': model,\n",
    "            'y_train_pred': data['y_train_pred'],\n",
    "            'y_test_pred': data['y_test_pred']\n",
    "        }, f)\n",
    "else:\n",
    "    with open('./../data/tmp/21_model_pred.pkl', 'rb') as f:\n",
    "        tmp = pickle.load(f)\n",
    "        model = tmp['model']\n",
    "        data['y_train_pred'] = tmp['y_train_pred']\n",
    "        data['y_test_pred'] = tmp['y_test_pred']\n",
    "        del(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = plt.subplot2grid((1, 2), (0, 0))\n",
    "ax2 = plt.subplot2grid((1, 2), (0, 1))\n",
    "\n",
    "sns.distplot(data['y_train_pred'], label='train', ax=ax1)\n",
    "sns.distplot(data['y_test_pred'], label='test', ax=ax1)\n",
    "ax1.axvline(np.mean(data['y_train_pred']), color='b', alpha=1, linestyle='--', label='mean(train)')\n",
    "ax1.axvline(np.mean(data['y_test_pred']), color='g', alpha=1, linestyle='--', label='mean(test)')\n",
    "ax1.legend(loc='upper right', prop={'size': 18})\n",
    "ax1.set_title('Train/test OOF predictions')\n",
    "\n",
    "sns.distplot(data['y_train_pred'][:ix_train.shape[0]//2], label='train first part', ax=ax2)\n",
    "sns.distplot(data['y_train_pred'][ix_train.shape[0]//2:], label='train second part', ax=ax2)\n",
    "ax2.legend(loc='upper right', prop={'size': 18})\n",
    "ax2.set_title('Train/train OOF predictions')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mp = np.mean(data['y_train_pred'])\n",
    "print (mp)\n",
    "\n",
    "def func(w):\n",
    "    return (mp*data['y_test_pred'].shape[0] - \n",
    "            np.sum(w[0]*data['y_test_pred']/(w[0]*data['y_test_pred'] + \n",
    "                                             (1 - w[0]) * (1 - data['y_test_pred']))))**2\n",
    "\n",
    "print (func(np.array([1])))\n",
    "\n",
    "res = minimize(func, np.array([1]), method='L-BFGS-B', bounds=[(0, 1)])\n",
    "\n",
    "print (res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "w = res['x'][0]\n",
    "\n",
    "def fix_function(x):\n",
    "    return w*x/(w*x + (1 - w)*(1 - x))\n",
    "\n",
    "support = np.linspace(0, 1, 1000)\n",
    "values = fix_function(support)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(support, values)\n",
    "ax.set_title('Fix transformation', fontsize=20)\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('f(x)')\n",
    "plt.show()\n",
    "\n",
    "data['y_test_pred_fixed'] = fix_function(data['y_test_pred'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = plt.subplot2grid((1, 2), (0, 0))\n",
    "ax2 = plt.subplot2grid((1, 2), (0, 1))\n",
    "\n",
    "sns.distplot(data['y_train_pred'], label='train', ax=ax1)\n",
    "sns.distplot(data['y_test_pred_fixed'], label='test', ax=ax1)\n",
    "ax1.axvline(np.mean(data['y_train_pred']), color='b', alpha=1, linestyle='--', label='mean(train)')\n",
    "ax1.axvline(np.mean(data['y_test_pred_fixed']), color='g', alpha=1, linestyle='--', label='mean(test)')\n",
    "ax1.legend(loc='upper right', prop={'size': 18})\n",
    "ax1.set_title('Train/test OOF predictions')\n",
    "\n",
    "sns.distplot(data['y_train_pred'][:ix_train.shape[0]//2], label='train first part', ax=ax2)\n",
    "sns.distplot(data['y_train_pred'][ix_train.shape[0]//2:], label='train second part', ax=ax2)\n",
    "ax2.legend(loc='upper right', prop={'size': 18})\n",
    "ax2.set_title('Train/train OOF predictions')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['m_w1l_tfidf_oof'] = np.zeros(df.shape[0])\n",
    "df.loc[ix_train, 'm_w1l_tfidf_oof'] = data['y_train_pred']\n",
    "df.loc[ix_test, 'm_w1l_tfidf_oof'] = data['y_test_pred_fixed']\n",
    "del(data)\n",
    "plot_real_feature('m_w1l_tfidf_oof')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictors = df.columns[7:]\n",
    "print (predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class QuoraKFold():\n",
    "    \n",
    "    def __init__(self, n_splits=10, r_split=0.7):\n",
    "        self.n_splits = n_splits\n",
    "        self.r_split = r_split\n",
    "        \n",
    "    def get_n_splits(self, X=None, y=None, groups=None):\n",
    "        return self.n_splits\n",
    "        \n",
    "    def split(self, X, y, groups=None):\n",
    "        if self.r_split is None:\n",
    "            self.r_split = 1 - 1.0/self.n_splits\n",
    "        k = r1*d[0]/(r0*d[1])\n",
    "        ix_ones = np.where(y == 1)[0]\n",
    "        ix_zeros = np.where(y == 0)[0]\n",
    "        for i in range(self.n_splits):\n",
    "            ix_first_zeros = np.random.choice(\n",
    "                ix_zeros, \n",
    "                size=int(self.r_split * ix_zeros.shape[0]), \n",
    "                replace=False)\n",
    "            ix_first_ones = np.random.choice(\n",
    "                ix_ones,\n",
    "                size=int(ix_first_zeros.shape[0] * d[1]/d[0]),\n",
    "                replace=False)\n",
    "            \n",
    "            ix_second_zeros = np.setdiff1d(ix_zeros, ix_first_zeros)\n",
    "            ix_second_ones = np.random.choice(\n",
    "                np.setdiff1d(ix_ones, ix_first_ones),\n",
    "                size=int(ix_second_zeros.shape[0] * r1/r0),\n",
    "                replace=False)\n",
    "            \n",
    "            ix_first = np.hstack((ix_first_zeros, ix_first_ones))\n",
    "            ix_first = ix_first[np.random.choice(\n",
    "                range(ix_first.shape[0]), size=ix_first.shape[0], replace=False)]\n",
    "            \n",
    "            ix_second = np.hstack((ix_second_zeros, ix_second_ones))\n",
    "            ix_second = ix_second[np.random.choice(\n",
    "                range(ix_second.shape[0]), size=ix_second.shape[0], replace=False)]\n",
    "            \n",
    "            yield ix_first, ix_second\n",
    "\n",
    "\n",
    "def check_model(predictors, data=None, do_scaling=True, folder=None, r_split=0.7):\n",
    "    classifier = lambda: SGDClassifier(\n",
    "        loss='log', \n",
    "        penalty='elasticnet', \n",
    "        fit_intercept=True, \n",
    "        n_iter=100, \n",
    "        shuffle=True, \n",
    "        n_jobs=-1,\n",
    "        class_weight=None)\n",
    "\n",
    "    steps = []\n",
    "    if do_scaling:\n",
    "        steps.append(('ss', StandardScaler()))\n",
    "    steps.append(('en', classifier()))\n",
    "    \n",
    "    model = Pipeline(steps=steps)\n",
    "\n",
    "    parameters = {\n",
    "        'en__alpha': [0.00001, 0.0001, 0.001, 0.01, 0.02, 0.1, 0.5, 0.9, 1],\n",
    "        'en__l1_ratio': [0, 0.0001, 0.001, 0.01, 0.1, 0.3, 0.5, 0.75, 0.9, 1]\n",
    "    }\n",
    "\n",
    "    if folder is None:\n",
    "        folder = QuoraKFold(n_splits=10, r_split=r_split)\n",
    "    \n",
    "    grid_search = GridSearchCV(\n",
    "        model, \n",
    "        parameters, \n",
    "        cv=folder, \n",
    "        n_jobs=-1,\n",
    "        scoring=make_scorer(log_loss_lf, greater_is_better=False, needs_proba=True),\n",
    "        verbose=1)\n",
    "    if data is None:\n",
    "        grid_search = grid_search.fit(df.loc[ix_train][predictors], \n",
    "                                      df.loc[ix_train]['is_duplicate'])\n",
    "    else:\n",
    "        grid_search = grid_search.fit(data['X'], \n",
    "                                      data['y'])\n",
    "    \n",
    "    return grid_search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.isfile('./../data/tmp/22_model.pkl'):\n",
    "    model = check_model(None, data={\n",
    "        'X': df.loc[ix_train][predictors],\n",
    "        'y': df.loc[ix_train]['is_duplicate']\n",
    "    }, do_scaling=True, r_split=0.7)\n",
    "    print (model.best_score_)\n",
    "    print (model.best_params_)\n",
    "    with open('./../data/tmp/22_model.pkl', 'wb') as f:\n",
    "        pickle.dump(model, f)\n",
    "else:\n",
    "    with open('./../data/tmp/22_model.pkl', 'rb') as f:\n",
    "        model = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "Fitting 10 folds for each of 90 candidates, totalling 900 fits\n",
    "[Parallel(n_jobs=-1)]: Done  18 tasks      | elapsed:   43.1s\n",
    "[Parallel(n_jobs=-1)]: Done 168 tasks      | elapsed:  4.6min\n",
    "[Parallel(n_jobs=-1)]: Done 418 tasks      | elapsed: 11.0min\n",
    "[Parallel(n_jobs=-1)]: Done 768 tasks      | elapsed: 20.4min\n",
    "[Parallel(n_jobs=-1)]: Done 900 out of 900 | elapsed: 23.9min finished\n",
    "-0.307676722634\n",
    "{'en__l1_ratio': 1, 'en__alpha': 0.0001}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "display(\n",
    "    eli5.sklearn.explain_linear_classifier_weights(\n",
    "        model.best_estimator_.steps[1][1],\n",
    "        feature_names=predictors.tolist(),\n",
    "        top=len(predictors)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred = model.predict_proba(df.loc[ix_test, predictors])[:, 1]\n",
    "y_test_pred_fixed = link_function(y_test_pred)\n",
    "\n",
    "if not os.path.isfile('./../data/tmp/22_pred_fixed.csv'):\n",
    "    pd.DataFrame.from_records(\n",
    "        zip(df.loc[ix_test]['test_id'].values, \n",
    "            y_test_pred_fixed), \n",
    "        columns=['test_id', 'is_duplicate']).to_csv('./../data/tmp/22_pred_fixed.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = plt.subplot2grid((1, 2), (0, 0))\n",
    "ax2 = plt.subplot2grid((1, 2), (0, 1))\n",
    "sns.distplot(y_test_pred, ax=ax1)\n",
    "ax1.set_title('Model prediction')\n",
    "sns.distplot(y_test_pred_fixed, ax=ax2)\n",
    "ax2.set_title('Model prediction fixed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- внезапно хуже: **0.32406**\n",
    "- если не делать коррекцию среднего: **0.40287**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "del(m_q1, m_q2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Черная магия\n",
    "\n",
    "<img src=\"./../images/buben7.jpg\" width=\"70%\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictors = df.columns[7:]\n",
    "print (predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "xgb_models = []\n",
    "if not os.path.isfile('./../data/tmp/25_pred_fixed.csv'):\n",
    "    def log_loss_lf_xgb(y_pred, y_true):\n",
    "        return 'llf', log_loss(y_true.get_label(), link_function(y_pred), eps=eps)\n",
    "\n",
    "    xgb_params = {\n",
    "        'max_depth': 9, \n",
    "        'learning_rate': 0.1,\n",
    "        'n_estimators': 2500, \n",
    "        'objective': 'binary:logistic',\n",
    "        'nthread': 16, \n",
    "        'gamma': 0, \n",
    "        'subsample': 0.75, \n",
    "        'colsample_bytree': 0.75, \n",
    "        'colsample_bylevel': 1,\n",
    "        'reg_alpha': 0, \n",
    "        'reg_lambda': 1, \n",
    "        'scale_pos_weight': 1\n",
    "    }\n",
    "\n",
    "    y_test = []\n",
    "    folder = QuoraKFold(n_splits=10, r_split=0.7)\n",
    "    splits = folder.split(np.zeros(ix_train.shape[0]), df.loc[ix_train, 'is_duplicate'])\n",
    "    for ix_first, ix_second in tqdm_notebook(splits, total=10):\n",
    "\n",
    "        model = xgb.XGBClassifier(silent=True).set_params(**xgb_params)\n",
    "        model = model.fit(df.loc[ix_first, predictors], df.loc[ix_first, 'is_duplicate'], \n",
    "                          eval_set=[(df.loc[ix_second, predictors], df.loc[ix_second, 'is_duplicate'])], \n",
    "                          eval_metric=log_loss_lf_xgb,\n",
    "                          early_stopping_rounds=100, \n",
    "                          verbose=False)\n",
    "        y_test.append(model.predict_proba(df.loc[ix_test, predictors])[:, 1])\n",
    "        xgb_models.append(model)\n",
    "\n",
    "    y_test_pred = np.array(y_test).T.mean(axis=1)\n",
    "    y_test_pred_fixed = link_function(y_test_pred)\n",
    "    \n",
    "    pd.DataFrame.from_records(\n",
    "        zip(df.loc[ix_test]['test_id'].values, \n",
    "            y_test_pred_fixed), \n",
    "        columns=['test_id', 'is_duplicate']).to_csv('./../data/tmp/25_pred_fixed.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.isfile('./../data/tmp/bm1.pkl'):\n",
    "    with open('./../data/tmp/bm1.pkl', 'wb') as f:\n",
    "        pickle.dump(xgb_models, f)\n",
    "else:\n",
    "    with open('./../data/tmp/bm1.pkl', 'rb') as f:\n",
    "        xgb_models = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = xgb_models[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb.plot_importance(model)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- немного черной магии дает: **0.29936**\n",
    "- на момент сабмита это **244/1880**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [Yuriy Guts] Save derived features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pygoose import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "project = kg.Project.discover()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_list_id = '3rdparty_mephistopheies'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['abs_diff_len1_len2', 'log_abs_diff_len1_len2', 'ratio_len1_len2',\n",
       "       'log_ratio_len1_len2', 'unigram_jaccard', 'unigram_all_jaccard',\n",
       "       'unigram_all_jaccard_max', 'bigram_jaccard', 'bigram_all_jaccard',\n",
       "       'bigram_all_jaccard_max', 'trigram_jaccard', 'trigram_all_jaccard',\n",
       "       'trigram_all_jaccard_max', 'trigram_tfidf_cosine',\n",
       "       'trigram_tfidf_l2_euclidean', 'trigram_tfidf_l1_euclidean',\n",
       "       'trigram_tf_l2_euclidean', 'm_q1_q2_tf_oof', 'm_q1_q2_tf_svd0',\n",
       "       'm_q1_q2_tf_svd1', 'm_q1_q2_tf_svd100_oof', 'm_diff_q1_q2_tf_oof'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_names = df.columns[9:]\n",
    "feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = df.loc[ix_train][feature_names].values\n",
    "X_test = df.loc[ix_test][feature_names].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((404290, 22), (2345796, 22))"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names = [\n",
    "    'meph_' + feature_name\n",
    "    #for feature_name in predictors.tolist()\n",
    "    for feature_name in feature_names\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "project.save_features(X_train, X_test, feature_names, feature_list_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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